CaseStudy2 Attrition
Renu Karthikeyan
November/December 2023
Statement of Purpose:
I am pleased to present this case study on Frito Lay attrition, which aims to analyze and derive actionable insights from the workforce data at Frito Lay. The purpose of this study is to understand the factors influencing employee attrition, develop predictive models for attrition risk, and evaluate the performance of the predictive models.
Objectives:
- Data Exploration and Visualization: Conduct a thorough exploration of the available workforce data to gain insights into the distribution of various attributes.Visualize key trends and patterns related to attrition, employee demographics, and other relevant factors.
- Predictive Modeling: Use K-nearest neighbors (KNN) and Naive Bayes, to build predictive models for employee attrition.Evaluate the performance of each model and identify the most effective approach in predicting attrition risk. Create linear regression model to predict salary for employees, given all other predictors.
- Shiny App Development: Create an interactive Shiny app to visualize and communicate the insights derived from the analysis.Provide a user-friendly platform for stakeholders to explore the data and understand the implications of the findings.
- Communication and Collaboration: Effectively communicate the results and recommendations to stakeholders through clear and concise reports and presentations.
Load and Install Libraries
library(dplyr)
library(ggplot2)
library(caret)
library(aws.s3)
library(RCurl)
library(readr)
library(base)
library(tidyverse)
library(naniar)
library(class)
library(GGally)
library(e1071)
library(car)
library(fastDummies)Set AWS credentials, and Load in Datasets from Amazon S3 Bucket
Here, loading in the data from the AWS S3 Bucket. I did some slight clean up of the data, to exclude the “Over18” column in the original data set and the Attrition test data set. The column name for ID in the No Salary data set was not the same, so I adjusted that. Also, I did a check for any missing values. There are no missing values in any of the data sets, so there is no need for imputing or deleting of rows.
# Load the Attrition data set from S3
#s3_path <- "s3://msds.ds.6306.2/CaseStudy2-data.csv"
# Read the Attrition Data CSV file from S3
Attritiondata <- read.table(textConnection(getURL("https://msdsds6306.s3.us-east-2.amazonaws.com/CaseStudy2-data.csv")), sep =",", header =T)
head(Attritiondata,5)## ID Age Attrition BusinessTravel DailyRate Department
## 1 1 32 No Travel_Rarely 117 Sales
## 2 2 40 No Travel_Rarely 1308 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 4 4 32 No Travel_Rarely 801 Sales
## 5 5 24 No Travel_Frequently 567 Research & Development
## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
## 1 13 4 Life Sciences 1 859
## 2 14 3 Medical 1 1128
## 3 18 2 Life Sciences 1 1412
## 4 1 4 Marketing 1 2016
## 5 2 1 Technical Degree 1 1646
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Male 73 3 2
## 2 3 Male 44 2 5
## 3 3 Male 60 3 3
## 4 3 Female 48 3 3
## 5 1 Female 32 3 1
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 1 Sales Executive 4 Divorced 4403
## 2 Research Director 3 Single 19626
## 3 Manufacturing Director 4 Single 9362
## 4 Sales Executive 4 Married 10422
## 5 Research Scientist 4 Single 3760
## MonthlyRate NumCompaniesWorked Over18 OverTime PercentSalaryHike
## 1 9250 2 Y No 11
## 2 17544 1 Y No 14
## 3 19944 2 Y No 11
## 4 24032 1 Y No 19
## 5 17218 1 Y Yes 13
## PerformanceRating RelationshipSatisfaction StandardHours StockOptionLevel
## 1 3 3 80 1
## 2 3 1 80 0
## 3 3 3 80 0
## 4 3 3 80 2
## 5 3 3 80 0
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## 1 8 3 2 5
## 2 21 2 4 20
## 3 10 2 3 2
## 4 14 3 3 14
## 5 6 2 3 6
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## 1 2 0 3
## 2 7 4 9
## 3 2 2 2
## 4 10 5 7
## 5 3 1 3#summary(Attritiondata)
vis_miss(Attritiondata) #checking for no missing values
#Read CSV "testing" files from S3
# Reading in of NoSalary Dataset from S3 Bucket
NoSalary<-read.table( textConnection(getURL
("https://msdsds6306.s3.us-east-2.amazonaws.com/CaseStudy2CompSet+No+Salary.csv"
)), sep=",", header=TRUE)
head(NoSalary,5)## ï..ID Age Attrition BusinessTravel DailyRate Department
## 1 871 43 No Travel_Frequently 1422 Sales
## 2 872 33 No Travel_Rarely 461 Research & Development
## 3 873 55 Yes Travel_Rarely 267 Sales
## 4 874 36 No Non-Travel 1351 Research & Development
## 5 875 27 No Travel_Rarely 1302 Research & Development
## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
## 1 2 4 Life Sciences 1 1849
## 2 13 1 Life Sciences 1 995
## 3 13 4 Marketing 1 1372
## 4 9 4 Life Sciences 1 1949
## 5 19 3 Other 1 1619
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 1 Male 92 3 2
## 2 2 Female 53 3 1
## 3 1 Male 85 4 4
## 4 1 Male 66 4 1
## 5 4 Male 67 2 1
## JobRole JobSatisfaction MaritalStatus MonthlyRate
## 1 Sales Executive 4 Married 19246
## 2 Research Scientist 4 Single 17241
## 3 Sales Executive 3 Single 9277
## 4 Laboratory Technician 2 Married 9238
## 5 Laboratory Technician 1 Divorced 16290
## NumCompaniesWorked Over18 OverTime PercentSalaryHike PerformanceRating
## 1 1 Y No 20 4
## 2 3 Y No 18 3
## 3 6 Y Yes 17 3
## 4 1 Y No 22 4
## 5 1 Y No 11 3
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## 1 3 80 1 7
## 2 1 80 0 5
## 3 3 80 0 24
## 4 2 80 0 5
## 5 1 80 2 7
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 1 5 3 7 7
## 2 4 3 3 2
## 3 2 2 19 7
## 4 3 3 5 4
## 5 3 3 7 7
## YearsSinceLastPromotion YearsWithCurrManager
## 1 7 7
## 2 0 2
## 3 3 8
## 4 0 2
## 5 0 7#summary(NoSalary)
vis_miss(NoSalary)
AttritionTest<- read.table(textConnection(getURL
("https://msdsds6306.s3.us-east-2.amazonaws.com/CaseStudy2CompSet+No+Attrition.csv"
)), sep=",", header=TRUE)
head(AttritionTest,5)## ID Age BusinessTravel DailyRate Department DistanceFromHome
## 1 1171 35 Travel_Rarely 750 Research & Development 28
## 2 1172 33 Travel_Rarely 147 Human Resources 2
## 3 1173 26 Travel_Rarely 1330 Research & Development 21
## 4 1174 55 Travel_Rarely 1311 Research & Development 2
## 5 1175 29 Travel_Rarely 1246 Sales 19
## Education EducationField EmployeeCount EmployeeNumber
## 1 3 Life Sciences 1 1596
## 2 3 Human Resources 1 1207
## 3 3 Medical 1 1107
## 4 3 Life Sciences 1 505
## 5 3 Life Sciences 1 1497
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Male 46 4 2
## 2 2 Male 99 3 1
## 3 1 Male 37 3 1
## 4 3 Female 97 3 4
## 5 3 Male 77 2 2
## JobRole JobSatisfaction MaritalStatus MonthlyIncome MonthlyRate
## 1 Laboratory Technician 3 Married 3407 25348
## 2 Human Resources 3 Married 3600 8429
## 3 Laboratory Technician 3 Divorced 2377 19373
## 4 Manager 4 Single 16659 23258
## 5 Sales Executive 3 Divorced 8620 23757
## NumCompaniesWorked Over18 OverTime PercentSalaryHike PerformanceRating
## 1 1 Y No 17 3
## 2 1 Y No 13 3
## 3 1 Y No 20 4
## 4 2 Y Yes 13 3
## 5 1 Y No 14 3
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## 1 4 80 2 10
## 2 4 80 1 5
## 3 3 80 1 1
## 4 3 80 0 30
## 5 3 80 2 10
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 1 3 2 10 9
## 2 2 3 5 4
## 3 0 2 1 1
## 4 2 3 5 4
## 5 3 3 10 7
## YearsSinceLastPromotion YearsWithCurrManager
## 1 6 8
## 2 1 4
## 3 0 0
## 4 1 2
## 5 0 4#summary(AttritionTest)
vis_miss(AttritionTest)
Attritiondata <- subset(Attritiondata, select = -c(Over18))
head(Attritiondata,5)## ID Age Attrition BusinessTravel DailyRate Department
## 1 1 32 No Travel_Rarely 117 Sales
## 2 2 40 No Travel_Rarely 1308 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 4 4 32 No Travel_Rarely 801 Sales
## 5 5 24 No Travel_Frequently 567 Research & Development
## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
## 1 13 4 Life Sciences 1 859
## 2 14 3 Medical 1 1128
## 3 18 2 Life Sciences 1 1412
## 4 1 4 Marketing 1 2016
## 5 2 1 Technical Degree 1 1646
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Male 73 3 2
## 2 3 Male 44 2 5
## 3 3 Male 60 3 3
## 4 3 Female 48 3 3
## 5 1 Female 32 3 1
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 1 Sales Executive 4 Divorced 4403
## 2 Research Director 3 Single 19626
## 3 Manufacturing Director 4 Single 9362
## 4 Sales Executive 4 Married 10422
## 5 Research Scientist 4 Single 3760
## MonthlyRate NumCompaniesWorked OverTime PercentSalaryHike PerformanceRating
## 1 9250 2 No 11 3
## 2 17544 1 No 14 3
## 3 19944 2 No 11 3
## 4 24032 1 No 19 3
## 5 17218 1 Yes 13 3
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## 1 3 80 1 8
## 2 1 80 0 21
## 3 3 80 0 10
## 4 3 80 2 14
## 5 3 80 0 6
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 1 3 2 5 2
## 2 2 4 20 7
## 3 2 3 2 2
## 4 3 3 14 10
## 5 2 3 6 3
## YearsSinceLastPromotion YearsWithCurrManager
## 1 0 3
## 2 4 9
## 3 2 2
## 4 5 7
## 5 1 3colnames(NoSalary)[colnames(NoSalary)=="ï..ID"] <- "ID"
colnames(NoSalary)## [1] "ID" "Age"
## [3] "Attrition" "BusinessTravel"
## [5] "DailyRate" "Department"
## [7] "DistanceFromHome" "Education"
## [9] "EducationField" "EmployeeCount"
## [11] "EmployeeNumber" "EnvironmentSatisfaction"
## [13] "Gender" "HourlyRate"
## [15] "JobInvolvement" "JobLevel"
## [17] "JobRole" "JobSatisfaction"
## [19] "MaritalStatus" "MonthlyRate"
## [21] "NumCompaniesWorked" "Over18"
## [23] "OverTime" "PercentSalaryHike"
## [25] "PerformanceRating" "RelationshipSatisfaction"
## [27] "StandardHours" "StockOptionLevel"
## [29] "TotalWorkingYears" "TrainingTimesLastYear"
## [31] "WorkLifeBalance" "YearsAtCompany"
## [33] "YearsInCurrentRole" "YearsSinceLastPromotion"
## [35] "YearsWithCurrManager"AttritionTest <- subset(AttritionTest, select = -c(Over18))
head(AttritionTest,5)## ID Age BusinessTravel DailyRate Department DistanceFromHome
## 1 1171 35 Travel_Rarely 750 Research & Development 28
## 2 1172 33 Travel_Rarely 147 Human Resources 2
## 3 1173 26 Travel_Rarely 1330 Research & Development 21
## 4 1174 55 Travel_Rarely 1311 Research & Development 2
## 5 1175 29 Travel_Rarely 1246 Sales 19
## Education EducationField EmployeeCount EmployeeNumber
## 1 3 Life Sciences 1 1596
## 2 3 Human Resources 1 1207
## 3 3 Medical 1 1107
## 4 3 Life Sciences 1 505
## 5 3 Life Sciences 1 1497
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Male 46 4 2
## 2 2 Male 99 3 1
## 3 1 Male 37 3 1
## 4 3 Female 97 3 4
## 5 3 Male 77 2 2
## JobRole JobSatisfaction MaritalStatus MonthlyIncome MonthlyRate
## 1 Laboratory Technician 3 Married 3407 25348
## 2 Human Resources 3 Married 3600 8429
## 3 Laboratory Technician 3 Divorced 2377 19373
## 4 Manager 4 Single 16659 23258
## 5 Sales Executive 3 Divorced 8620 23757
## NumCompaniesWorked OverTime PercentSalaryHike PerformanceRating
## 1 1 No 17 3
## 2 1 No 13 3
## 3 1 No 20 4
## 4 2 Yes 13 3
## 5 1 No 14 3
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## 1 4 80 2 10
## 2 4 80 1 5
## 3 3 80 1 1
## 4 3 80 0 30
## 5 3 80 2 10
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 1 3 2 10 9
## 2 2 3 5 4
## 3 0 2 1 1
## 4 2 3 5 4
## 5 3 3 10 7
## YearsSinceLastPromotion YearsWithCurrManager
## 1 6 8
## 2 1 4
## 3 0 0
## 4 1 2
## 5 0 4EDA
Looking at relationships within overall data, not just those attrited
ggplot(data=Attritiondata, aes(x=JobSatisfaction)) +geom_bar(position="dodge") + theme_minimal() + ggtitle("Overall Job Satisfaction")
Here, we look at overall Job Satisfaction among the employees. It looks
like majority of employees seem to be satisfied with their job, with a
handful not being as satisfied. It is a left skewed histogram.
ggplot(data=Attritiondata,aes(x=MonthlyIncome, y=Age)) + geom_point(position="jitter") + facet_wrap(~MaritalStatus)+geom_smooth(method="loess") + ggtitle("Monthly Income and Age categorized by Marital Status")
Here, we see Monthly Income by Age categorized by Marital Status.
Starting off, it looks like Divorced and Married people tend to make
more as their age increases. However, the same positive trend is seen
for Single people, but they do not make as much as their Married or
Divorced coworkers. But there is definitely a positive trend between age
and monthly income.
ggplot(data = Attritiondata, aes(x = MonthlyIncome, y = Age, color = JobInvolvement)) +
geom_point(position = "jitter") +
geom_smooth(method = lm) +
ggtitle("Job Involvement and Monthly Income") + facet_wrap(~JobInvolvement)
Looking at job involvement and monthly Income, it looks most employees
regardless of Job Involvement have a positive correlation between Age
and Monthly Income. I was trying to see if those who were more involved
in their job made a higher income, but that does not seem to be the case
at first glance. It looks like a lot of employees are pretty involved in
their jobs (Job Involvement levels 2 and 3).
ggplot(data = Attritiondata, aes(x = MonthlyIncome, y = Age, color = interaction(JobInvolvement))) +
geom_point(position = "jitter") +
geom_smooth(method = lm, se = FALSE) +
ggtitle("Job Involvement and Monthly Income")
When I plotted all 4 job levels with their respective linear regression
lines, it looks like those with least job involvement (Job Involvement
1) start to make more after crossing 40 years old and a monthly income
of 10,000. It seems like Job Involvement 3 make the most starting off up
to a monthly income of 10,000 and ~40 years old, and start to make the
least as they near 50 years old. Job Involvements 2 and 4 linear
regression lines fall in between the 1 and 4 lines initially and towards
the end, they are very close to each otehr, and seem to overlap.
ggplot(data=Attritiondata,aes(x=MonthlyIncome,y=NumCompaniesWorked)) + geom_point(position="jitter") + geom_smooth(method=lm) + ggtitle("Number of Companies Worked and Monthly Income")
There is positive correlation between Monthly Income and Number of
Companies worked.
ggplot(data = Attritiondata, aes(x = MonthlyIncome)) + geom_histogram() + ggtitle("Monthly Income Histogram")
Looking at a histogram of Monthly Income, it looks to be right skewed.
The mode is less than the median which is less than the mean.
ggplot(data=Attritiondata,aes(x=MonthlyIncome)) + geom_histogram() + ggtitle("Monthly Income Histogram Categorized by Gender") + facet_wrap(~Gender)
Looking at monthly income categorized by Gender, it looks like there are
more male datapoints in the dataset than femalses. The mode is higher
for men than it is for women. Both histograms are right skewed.
ggplot(data=Attritiondata,aes(x=MonthlyIncome, y=DistanceFromHome)) + geom_point(position="jitter") + geom_smooth(method=loess) + ggtitle("Monthly Income and Distance from Home")
There seems to be a negative correlation between distance from home and
monthly income. The plot and loess curve imply that as distance from
home initially increases, monthly income also increases, but after a
monthly income of 15,000 is exceeded, there is an overall decrease in
the distance from home. The curve resembles a concave curve, with the
downward slight ‘w’ shape. Looking at this graph, I would interpret
distance from home to be in miles, because if this is in kilometers, it
doesn’t seem to make logical sense.
ggplot(data=Attritiondata,aes(x=MonthlyIncome, y=DistanceFromHome)) + geom_point(position="jitter") + geom_smooth(method=loess) + facet_wrap(~Gender) + ggtitle("Monthly Income and Distance from Home categorized by Gender")
There seems to be a more prominent downturned ‘w’ shape for women than
for men. But the overall relationship is as mentioned above (between
distance from home and Monthly Income).
ggplot(data=Attritiondata, aes(x=Department, y=JobSatisfaction, color = Gender)) + geom_point(position ="jitter") + facet_wrap(~Gender) + ggtitle("Department and Job Satisfaction by Gender")
Job Satisfaction seems to be higher for Research and Development for
both Males and Females. There are less data points for those in Human
Resources so a clear defined relationship can’t be concluded. There
seems to be decent job satisfaction for those in Sales too for both
genders.
ggplot(data=Attritiondata,aes(x=Age, y=TotalWorkingYears, color = MonthlyIncome)) + geom_point(position = "jitter") + ggtitle("Age and Total Working Years with Monthly Income")
This plot confirms that as Age increases, total working years also
increase, and monthly income seems to follow the same trend as well.
There is a positive correlation between all 3 variables - Age, Total
working years, and monthly income.
Attrition Specific Analysis
I filtered the data to look at specifically those who Attrited, to find some insights and relationships between the variables.
attrition_yes <- dplyr::filter(Attritiondata, Attrition == "Yes")ggplot(data = attrition_yes, aes(x = Department, fill = Gender))+ geom_bar(position = "dodge") +
ggtitle("Attrition by Department and Gender") + theme_minimal()
Of those who left the company, many men were in Research &
Development and Sales, while there were equal amounts of women in
Research & Development and Sales. There are equal amounts of men and
women from Human resources who left the company.
ggplot(data = attrition_yes, aes(y = JobSatisfaction , x = DistanceFromHome, color = MonthlyIncome))+
geom_point(position = "jitter") + theme_minimal() + geom_smooth(method =lm) + ggtitle("Attrition by Distance from Home, Job Satisfaction with Monthly Income")
Of those who left the company, it seems likes a lot of them were making
under $10,000 monthly. There seems to be a negative relationship between
Job Satisfaction and Distance from home (as distance from home increases
in miles, the job satisfaction goes down).
ggplot(data = attrition_yes, aes(y=JobLevel, x = MonthlyIncome, color = Age))+
geom_point(position="jitter")+geom_smooth(method=lm) + ggtitle("Monthly Income and Job Level with Age")
Of those who left the company, there is a positive relationship between
job level and Monthly income. Age seems to be scattered, but at a job
level of around 1, and monthly income less than 5000, the age group
seems to have employees in their 20s.
ggplot(data = attrition_yes, aes(y=JobLevel, x = Age, color = Gender))+
geom_point(position="jitter")+geom_smooth(method=lm) + ggtitle("Attrition Job Level by Age and Gender")
OF those who left the company, it looks like Job Level is positively
correlated with Age for both Males and Females. The slope of the linear
regression line for females seems to be more steep than it is for the
males.
ggplot(data = attrition_yes, aes(x=OverTime, fill = Gender)) + geom_bar() + ggtitle("Attrition - Overtime by Gender")
Of those who left the company, many employees were working over time. As
mentioned earlier, there are more male data points compared to females,
which is why at first glance it may seem a bit off. It looks like of the
Over time group - approximately 70% were males, and 30% was females.
ggplot(data = attrition_yes, aes(x=NumCompaniesWorked, y = PercentSalaryHike)) + geom_point(position = "jitter") + theme_minimal()+geom_smooth(method = lm) + ggtitle ("Attrition - Percent Salary Hike and Number of Companies Worked")
There seems to be a slight downward trend when looking at the
relationship between Number of companies worked and Percent salary hike.
I was trying to see if the number of companies worked affected the
percent salary hike positively. At first glance, it seems like there is
no variation int he line, but if you observe closely, there is a slight
downward trend.
ggplot(data = attrition_yes, aes(x = PercentSalaryHike, fill = OverTime)) +
geom_histogram( binwidth = 1, color = "black", alpha = 0.7) +
geom_density(aes(y = ..count..), fill = "transparent", color = "darkblue") +
labs(title = "Histogram with Trend Line of % Salary Hike by Overtime",
x = "Percent Salary Hike", y = "Count") +
theme_minimal() + theme(legend.position = "top") # Adjust legend position
This is a histogram of percent salary hike, with each bar being split
and shaded by if the employee(s) were working over time. The overall
percent salary hike (without the split) seems to be right skewed
generally, but the shape of the line/trend seems to imply that it may be
multimodal.
ggplot(data = attrition_yes, aes(x=YearsAtCompany, y = PercentSalaryHike, color = PerformanceRating)) + geom_point() + theme_minimal() + ggtitle("Salary Hike v. Years at Company with Performance Rating")
This plot takes a look at Years at Company and Percent salary hike.
Those with a higher performance rating seem to have a higher percent
salary hike. For a Percent salary hike less than 20%, it seems like the
performance rating is under 4, and trends around the 3 rating.
ggplot(data = attrition_yes, aes(y=JobSatisfaction, x = Education)) + geom_point(position="jitter") + theme_minimal() + ggtitle("Job Satisfaction v. Education Categorized by Education Field") + facet_wrap(~EducationField) + geom_smooth(method = lm)
This plot looks at the relationship between Job Satisfaction based on
Education categorized by education field. Most education fields seem to
have a negative correlation between job satisfaction and years of
education, except for the Medical field. The medical field is the only
field where as education increases, the job satifaction also seems to
increase.
#Building a Regression Model to Determine Salary
Regression Model 1 Using All Predictors to Determine Salary
Monthly Income is the “salary” variable
class(Attritiondata$MonthlyIncome)## [1] "integer"sum(is.na(Attritiondata$MonthlyIncome))## [1] 0summary(Attritiondata)## ID Age Attrition BusinessTravel
## Min. : 1.0 Min. :18.00 Length:870 Length:870
## 1st Qu.:218.2 1st Qu.:30.00 Class :character Class :character
## Median :435.5 Median :35.00 Mode :character Mode :character
## Mean :435.5 Mean :36.83
## 3rd Qu.:652.8 3rd Qu.:43.00
## Max. :870.0 Max. :60.00
## DailyRate Department DistanceFromHome Education
## Min. : 103.0 Length:870 Min. : 1.000 Min. :1.000
## 1st Qu.: 472.5 Class :character 1st Qu.: 2.000 1st Qu.:2.000
## Median : 817.5 Mode :character Median : 7.000 Median :3.000
## Mean : 815.2 Mean : 9.339 Mean :2.901
## 3rd Qu.:1165.8 3rd Qu.:14.000 3rd Qu.:4.000
## Max. :1499.0 Max. :29.000 Max. :5.000
## EducationField EmployeeCount EmployeeNumber EnvironmentSatisfaction
## Length:870 Min. :1 Min. : 1.0 Min. :1.000
## Class :character 1st Qu.:1 1st Qu.: 477.2 1st Qu.:2.000
## Mode :character Median :1 Median :1039.0 Median :3.000
## Mean :1 Mean :1029.8 Mean :2.701
## 3rd Qu.:1 3rd Qu.:1561.5 3rd Qu.:4.000
## Max. :1 Max. :2064.0 Max. :4.000
## Gender HourlyRate JobInvolvement JobLevel
## Length:870 Min. : 30.00 Min. :1.000 Min. :1.000
## Class :character 1st Qu.: 48.00 1st Qu.:2.000 1st Qu.:1.000
## Mode :character Median : 66.00 Median :3.000 Median :2.000
## Mean : 65.61 Mean :2.723 Mean :2.039
## 3rd Qu.: 83.00 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :100.00 Max. :4.000 Max. :5.000
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## Length:870 Min. :1.000 Length:870 Min. : 1081
## Class :character 1st Qu.:2.000 Class :character 1st Qu.: 2840
## Mode :character Median :3.000 Mode :character Median : 4946
## Mean :2.709 Mean : 6390
## 3rd Qu.:4.000 3rd Qu.: 8182
## Max. :4.000 Max. :19999
## MonthlyRate NumCompaniesWorked OverTime PercentSalaryHike
## Min. : 2094 Min. :0.000 Length:870 Min. :11.0
## 1st Qu.: 8092 1st Qu.:1.000 Class :character 1st Qu.:12.0
## Median :14074 Median :2.000 Mode :character Median :14.0
## Mean :14326 Mean :2.728 Mean :15.2
## 3rd Qu.:20456 3rd Qu.:4.000 3rd Qu.:18.0
## Max. :26997 Max. :9.000 Max. :25.0
## PerformanceRating RelationshipSatisfaction StandardHours StockOptionLevel
## Min. :3.000 Min. :1.000 Min. :80 Min. :0.0000
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:80 1st Qu.:0.0000
## Median :3.000 Median :3.000 Median :80 Median :1.0000
## Mean :3.152 Mean :2.707 Mean :80 Mean :0.7839
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:80 3rd Qu.:1.0000
## Max. :4.000 Max. :4.000 Max. :80 Max. :3.0000
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## Min. : 0.00 Min. :0.000 Min. :1.000 Min. : 0.000
## 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.: 3.000
## Median :10.00 Median :3.000 Median :3.000 Median : 5.000
## Mean :11.05 Mean :2.832 Mean :2.782 Mean : 6.962
## 3rd Qu.:15.00 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:10.000
## Max. :40.00 Max. :6.000 Max. :4.000 Max. :40.000
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## Min. : 0.000 Min. : 0.000 Min. : 0.00
## 1st Qu.: 2.000 1st Qu.: 0.000 1st Qu.: 2.00
## Median : 3.000 Median : 1.000 Median : 3.00
## Mean : 4.205 Mean : 2.169 Mean : 4.14
## 3rd Qu.: 7.000 3rd Qu.: 3.000 3rd Qu.: 7.00
## Max. :18.000 Max. :15.000 Max. :17.00# Identify character variables
char_vars <- sapply(Attritiondata, is.character)
# Convert character variables to factors
Attritiondata[, char_vars] <- lapply(Attritiondata[, char_vars], as.factor)
#Check Factor Levels for Categorical variables:
sapply(Attritiondata[,char_vars],levels)## $Attrition
## [1] "No" "Yes"
##
## $BusinessTravel
## [1] "Non-Travel" "Travel_Frequently" "Travel_Rarely"
##
## $Department
## [1] "Human Resources" "Research & Development" "Sales"
##
## $EducationField
## [1] "Human Resources" "Life Sciences" "Marketing" "Medical"
## [5] "Other" "Technical Degree"
##
## $Gender
## [1] "Female" "Male"
##
## $JobRole
## [1] "Healthcare Representative" "Human Resources"
## [3] "Laboratory Technician" "Manager"
## [5] "Manufacturing Director" "Research Director"
## [7] "Research Scientist" "Sales Executive"
## [9] "Sales Representative"
##
## $MaritalStatus
## [1] "Divorced" "Married" "Single"
##
## $OverTime
## [1] "No" "Yes" #Noticed Over18 has only 1 factor level; so going to remove from dataset
# Attritiondata <- subset(Attritiondata, select = -c(Over18))
# Fit the linear regression model with all predictors
Model1_fit <- lm(MonthlyIncome ~ ., data = Attritiondata)
summary(Model1_fit)##
## Call:
## lm(formula = MonthlyIncome ~ ., data = Attritiondata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3708.8 -674.1 14.7 614.1 4100.0
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.013e+01 7.772e+02 0.077 0.938349
## ID -2.343e-01 1.476e-01 -1.588 0.112713
## Age -1.431e+00 5.649e+00 -0.253 0.800110
## AttritionYes 8.904e+01 1.154e+02 0.771 0.440729
## BusinessTravelTravel_Frequently 1.895e+02 1.420e+02 1.334 0.182441
## BusinessTravelTravel_Rarely 3.720e+02 1.200e+02 3.099 0.002005 **
## DailyRate 1.452e-01 9.129e-02 1.591 0.112062
## DepartmentResearch & Development 1.234e+02 4.768e+02 0.259 0.795866
## DepartmentSales -4.594e+02 4.877e+02 -0.942 0.346580
## DistanceFromHome -6.237e+00 4.578e+00 -1.362 0.173417
## Education -3.743e+01 3.716e+01 -1.007 0.314105
## EducationFieldLife Sciences 1.352e+02 3.692e+02 0.366 0.714248
## EducationFieldMarketing 1.377e+02 3.914e+02 0.352 0.725050
## EducationFieldMedical 3.326e+01 3.699e+02 0.090 0.928376
## EducationFieldOther 9.152e+01 3.946e+02 0.232 0.816664
## EducationFieldTechnical Degree 9.680e+01 3.843e+02 0.252 0.801179
## EmployeeCount NA NA NA NA
## EmployeeNumber 8.681e-02 6.103e-02 1.422 0.155269
## EnvironmentSatisfaction -6.267e+00 3.364e+01 -0.186 0.852252
## GenderMale 1.100e+02 7.442e+01 1.478 0.139715
## HourlyRate -3.591e-01 1.824e+00 -0.197 0.844003
## JobInvolvement 1.677e+01 5.321e+01 0.315 0.752698
## JobLevel 2.783e+03 8.340e+01 33.375 < 2e-16 ***
## JobRoleHuman Resources -2.053e+02 5.157e+02 -0.398 0.690663
## JobRoleLaboratory Technician -5.891e+02 1.714e+02 -3.437 0.000618 ***
## JobRoleManager 4.280e+03 2.830e+02 15.122 < 2e-16 ***
## JobRoleManufacturing Director 1.809e+02 1.696e+02 1.067 0.286497
## JobRoleResearch Director 4.077e+03 2.193e+02 18.592 < 2e-16 ***
## JobRoleResearch Scientist -3.494e+02 1.705e+02 -2.049 0.040790 *
## JobRoleSales Executive 5.263e+02 3.576e+02 1.472 0.141449
## JobRoleSales Representative 8.531e+01 3.918e+02 0.218 0.827703
## JobSatisfaction 3.278e+01 3.344e+01 0.980 0.327278
## MaritalStatusMarried 6.708e+01 1.002e+02 0.669 0.503497
## MaritalStatusSingle 1.128e+01 1.361e+02 0.083 0.933978
## MonthlyRate -9.505e-03 5.143e-03 -1.848 0.064946 .
## NumCompaniesWorked 5.421e+00 1.691e+01 0.321 0.748622
## OverTimeYes -1.394e+01 8.434e+01 -0.165 0.868787
## PercentSalaryHike 2.586e+01 1.581e+01 1.635 0.102351
## PerformanceRating -3.235e+02 1.614e+02 -2.004 0.045368 *
## RelationshipSatisfaction 1.640e+01 3.339e+01 0.491 0.623375
## StandardHours NA NA NA NA
## StockOptionLevel -2.758e+00 5.740e+01 -0.048 0.961695
## TotalWorkingYears 5.080e+01 1.098e+01 4.627 4.3e-06 ***
## TrainingTimesLastYear 2.436e+01 2.912e+01 0.837 0.403111
## WorkLifeBalance -3.472e+01 5.161e+01 -0.673 0.501284
## YearsAtCompany -2.750e+00 1.370e+01 -0.201 0.840925
## YearsInCurrentRole 3.398e+00 1.711e+01 0.199 0.842584
## YearsSinceLastPromotion 3.084e+01 1.532e+01 2.013 0.044405 *
## YearsWithCurrManager -2.691e+01 1.669e+01 -1.613 0.107210
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1055 on 823 degrees of freedom
## Multiple R-squared: 0.9501, Adjusted R-squared: 0.9473
## F-statistic: 340.7 on 46 and 823 DF, p-value: < 2.2e-16#p val < alpha of .05, it affects the Salary Variable
#Model1_Preds = predict(Model1_fit, newdata = NoSalary) #this is an example of predict function you would want to use
#as.data.frame(Model1_Preds)
#write.csv(Model1_Preds,"Model1PredictionsNoSalaryRenuKarthikeyan.csv")Looking at this summary of model 1 output, it indicates that the statistically significant p values are Business Travel, JobLevel, Job Role, Performance rating, Total working Years, and Years since last promotion. The F-statistic tests the overall significance of the model. The F-statistic is 340.7 with a very small p-value (< 2.2e-16), suggests that at least one predictor variable is significantly related to Monthly Income.There are two coefficients not defined because of singularities. This might indicate multicollinearity, where two or more predictor variables are highly correlated.
Linear Regression using Select predictors to Determine Salary
Model2_fit = lm(MonthlyIncome ~ NumCompaniesWorked + Age + Gender + MaritalStatus + JobInvolvement + JobRole + DistanceFromHome + JobLevel + Education, data = Attritiondata)
summary(Model2_fit) # P value overall implies that at least one of my variables' slope != 0. ##
## Call:
## lm(formula = MonthlyIncome ~ NumCompaniesWorked + Age + Gender +
## MaritalStatus + JobInvolvement + JobRole + DistanceFromHome +
## JobLevel + Education, data = Attritiondata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3536.4 -699.4 -39.1 659.8 4178.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -600.926 320.383 -1.876 0.06105 .
## NumCompaniesWorked 17.866 15.366 1.163 0.24526
## Age 12.875 4.962 2.595 0.00963 **
## GenderMale 120.277 75.308 1.597 0.11061
## MaritalStatusMarried 106.159 95.406 1.113 0.26615
## MaritalStatusSingle 23.451 103.944 0.226 0.82155
## JobInvolvement 15.469 52.613 0.294 0.76882
## JobRoleHuman Resources -327.849 255.950 -1.281 0.20057
## JobRoleLaboratory Technician -534.026 172.285 -3.100 0.00200 **
## JobRoleManager 3933.510 232.847 16.893 < 2e-16 ***
## JobRoleManufacturing Director 92.825 169.877 0.546 0.58492
## JobRoleResearch Director 3919.755 218.518 17.938 < 2e-16 ***
## JobRoleResearch Scientist -246.097 171.976 -1.431 0.15280
## JobRoleSales Executive -122.894 146.575 -0.838 0.40202
## JobRoleSales Representative -392.881 217.075 -1.810 0.07067 .
## DistanceFromHome -7.908 4.553 -1.737 0.08281 .
## JobLevel 3042.789 69.854 43.559 < 2e-16 ***
## Education -33.713 37.379 -0.902 0.36736
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1078 on 852 degrees of freedom
## Multiple R-squared: 0.9461, Adjusted R-squared: 0.945
## F-statistic: 878.9 on 17 and 852 DF, p-value: < 2.2e-16NoSalary$MonthlyIncome = predict(Model2_fit,newdata = NoSalary)
#Model2_Preds<- NoSalary %>% select(c("ID","MonthlyIncome"))
#as.data.frame(Model2_Preds)
#write.csv(Model2_Preds,"Model2PredictionsNoSalaryRenuKarthikeyan.csv", row.names = T)These select predictors were chosen because of the insights from the EDA. I thought they were significant predictors. The coefficient for JobLevel is 3042.789. This suggests that, on average, an increase of one unit in JobLevel is associated with an increase of 3042.789 dollars in MonthlyIncome.Likewise, Age has a coefficient of 12.875 indicating that a year increase in age, results in 12.88 dollars additional monthly income, holding all other variables constant. Males have a coefficient of 120, indicating that holding all other variables constant, males make an additional 120 dollars compared to women monthly. The coefficient for distance from home has a. -7.908, which indicates each additional mile away from home may result in a monthly salary decrease by -7 dollars.
The statistically significant p values (<.10) are Age, certain Job Roles – like Laboratory Technician, Manager, and Research director, Job Level;
Overall, Model 2 appears to have a high R-squared value, indicating a good fit to the data. Many predictors are statistically significant, suggesting they contribute to determining Monthly Income
Split and Train Linear Regression Models to Predict Salary, with average of cross validation
set.seed(1234)
TrainObs = sample(seq(1,dim(Attritiondata)[1]),round(.8*dim(Attritiondata)[1]),replace = FALSE)
SalaryTrain = Attritiondata[TrainObs,]
SalaryTrain## ID Age Attrition BusinessTravel DailyRate Department
## 284 284 31 No Travel_Rarely 691 Sales
## 848 848 39 No Travel_Rarely 1132 Research & Development
## 101 101 27 No Travel_Rarely 1377 Research & Development
## 623 623 34 No Travel_Rarely 182 Research & Development
## 645 645 41 Yes Non-Travel 906 Research & Development
## 400 400 44 No Travel_Frequently 1193 Research & Development
## 98 98 37 No Non-Travel 1040 Research & Development
## 103 103 34 No Non-Travel 1381 Sales
## 726 726 25 No Travel_Rarely 685 Research & Development
## 602 602 32 No Non-Travel 1200 Research & Development
## 326 326 27 No Travel_Rarely 618 Research & Development
## 79 79 45 No Non-Travel 1052 Sales
## 270 270 30 No Travel_Rarely 1176 Research & Development
## 382 382 26 No Travel_Rarely 1355 Human Resources
## 184 184 39 No Travel_Rarely 1089 Research & Development
## 574 574 34 No Travel_Rarely 810 Sales
## 4 4 32 No Travel_Rarely 801 Sales
## 661 661 32 No Travel_Frequently 430 Research & Development
## 552 552 28 No Travel_Rarely 1169 Human Resources
## 212 212 42 No Travel_Rarely 265 Sales
## 195 195 26 No Travel_Rarely 683 Research & Development
## 511 511 38 No Travel_Rarely 345 Sales
## 479 479 52 No Travel_Rarely 258 Research & Development
## 605 605 29 Yes Travel_Rarely 428 Sales
## 634 634 36 No Travel_Rarely 922 Research & Development
## 578 578 44 No Non-Travel 981 Research & Development
## 510 510 41 Yes Travel_Rarely 1360 Research & Development
## 687 687 29 Yes Travel_Frequently 337 Research & Development
## 424 424 18 No Non-Travel 1124 Research & Development
## 379 379 29 No Travel_Rarely 942 Research & Development
## 816 816 33 No Travel_Rarely 117 Research & Development
## 108 108 55 No Travel_Rarely 189 Human Resources
## 131 131 29 No Travel_Rarely 991 Sales
## 343 343 33 No Travel_Rarely 654 Research & Development
## 41 41 58 No Non-Travel 350 Sales
## 627 627 24 No Travel_Rarely 1476 Sales
## 740 740 35 No Travel_Rarely 538 Research & Development
## 298 298 34 Yes Travel_Frequently 658 Research & Development
## 811 811 30 No Non-Travel 111 Research & Development
## 258 258 30 No Travel_Rarely 855 Sales
## 629 629 55 No Travel_Frequently 1091 Research & Development
## 859 859 41 No Travel_Rarely 933 Research & Development
## 182 182 38 No Travel_Rarely 1009 Sales
## 305 305 58 Yes Travel_Rarely 289 Research & Development
## 358 358 29 Yes Travel_Rarely 408 Research & Development
## 696 696 47 No Travel_Rarely 1454 Sales
## 307 307 50 No Travel_Rarely 804 Research & Development
## 827 827 31 Yes Travel_Rarely 1079 Sales
## 221 221 34 No Travel_Rarely 304 Sales
## 736 736 27 No Travel_Rarely 511 Sales
## 561 561 36 No Travel_Frequently 1480 Research & Development
## 313 313 31 Yes Travel_Frequently 561 Research & Development
## 136 136 40 No Travel_Frequently 791 Research & Development
## 794 794 36 No Travel_Rarely 1223 Research & Development
## 145 145 40 No Travel_Rarely 1202 Research & Development
## 123 123 45 No Travel_Rarely 930 Sales
## 776 776 23 No Travel_Rarely 310 Research & Development
## 234 234 54 No Travel_Rarely 1147 Sales
## 608 608 38 No Travel_Rarely 322 Sales
## 495 495 36 No Travel_Rarely 1266 Sales
## 534 534 32 No Non-Travel 1146 Research & Development
## 803 803 39 No Travel_Rarely 117 Research & Development
## 297 297 34 No Travel_Frequently 1069 Research & Development
## 208 208 38 Yes Travel_Rarely 1180 Research & Development
## 838 838 34 No Travel_Rarely 258 Sales
## 569 569 42 No Travel_Rarely 462 Sales
## 522 522 28 Yes Travel_Rarely 329 Research & Development
## 248 248 32 No Travel_Rarely 234 Sales
## 365 365 55 No Travel_Rarely 282 Research & Development
## 665 665 34 No Non-Travel 1065 Sales
## 643 643 55 No Non-Travel 444 Research & Development
## 595 595 40 No Travel_Rarely 884 Research & Development
## 434 434 29 No Travel_Rarely 590 Research & Development
## 757 757 35 No Travel_Rarely 1137 Research & Development
## 760 760 45 Yes Travel_Rarely 1449 Sales
## 218 218 48 No Travel_Frequently 117 Research & Development
## 727 727 37 No Travel_Frequently 1231 Sales
## 508 508 51 No Travel_Frequently 541 Sales
## 276 276 56 No Travel_Rarely 718 Research & Development
## 169 169 52 No Travel_Rarely 956 Research & Development
## 71 71 41 No Travel_Rarely 642 Research & Development
## 573 573 28 No Travel_Frequently 783 Sales
## 860 860 44 Yes Travel_Rarely 621 Research & Development
## 485 485 58 Yes Travel_Rarely 147 Research & Development
## 667 667 29 No Travel_Frequently 1413 Sales
## 460 460 36 No Travel_Rarely 172 Research & Development
## 60 60 25 Yes Travel_Frequently 599 Sales
## 449 449 38 No Travel_Frequently 653 Research & Development
## 548 548 40 No Travel_Frequently 720 Research & Development
## 19 19 34 No Travel_Rarely 181 Research & Development
## 649 649 35 No Travel_Rarely 776 Sales
## 638 638 34 No Travel_Rarely 1239 Sales
## 670 670 54 No Travel_Rarely 685 Research & Development
## 319 319 39 No Travel_Rarely 1498 Sales
## 116 116 37 No Non-Travel 1252 Sales
## 840 840 48 No Travel_Rarely 1224 Research & Development
## 102 102 31 No Travel_Rarely 326 Sales
## 214 214 42 No Travel_Rarely 932 Research & Development
## 390 390 21 Yes Travel_Frequently 756 Sales
## 597 597 35 No Travel_Rarely 672 Research & Development
## 771 771 33 No Travel_Frequently 508 Sales
## 160 160 42 No Travel_Frequently 458 Research & Development
## 77 77 34 No Travel_Rarely 971 Sales
## 529 529 35 No Travel_Rarely 1214 Research & Development
## 126 126 35 No Travel_Rarely 1224 Sales
## 262 262 29 Yes Travel_Rarely 224 Research & Development
## 442 442 29 No Travel_Rarely 1082 Research & Development
## 642 642 40 No Travel_Rarely 750 Research & Development
## 181 181 35 No Travel_Rarely 819 Research & Development
## 163 163 33 No Travel_Rarely 1216 Sales
## 474 474 45 No Travel_Rarely 1316 Research & Development
## 228 228 22 No Travel_Rarely 1136 Research & Development
## 774 774 36 Yes Travel_Rarely 318 Research & Development
## 646 646 40 No Travel_Rarely 630 Sales
## 265 265 24 No Travel_Rarely 1127 Research & Development
## 427 427 30 No Non-Travel 990 Research & Development
## 781 781 41 Yes Travel_Rarely 1356 Sales
## 249 249 41 No Travel_Frequently 1200 Research & Development
## 810 810 27 No Travel_Rarely 1157 Research & Development
## 40 40 31 No Travel_Frequently 793 Sales
## 541 541 30 No Travel_Rarely 853 Research & Development
## 497 497 45 No Travel_Rarely 192 Research & Development
## 697 697 48 No Travel_Frequently 365 Research & Development
## 865 865 45 No Travel_Rarely 1448 Research & Development
## 450 450 45 No Non-Travel 336 Sales
## 600 600 24 Yes Travel_Rarely 813 Research & Development
## 778 778 32 No Non-Travel 862 Sales
## 363 363 52 Yes Travel_Rarely 266 Sales
## 478 478 26 No Travel_Rarely 775 Sales
## 403 403 47 No Travel_Rarely 207 Research & Development
## 375 375 20 No Travel_Rarely 727 Sales
## 335 335 59 No Non-Travel 1420 Human Resources
## 598 598 40 No Travel_Rarely 555 Research & Development
## 142 142 58 No Travel_Rarely 848 Research & Development
## 444 444 44 No Travel_Rarely 528 Human Resources
## 741 741 25 No Travel_Rarely 959 Sales
## 659 659 41 No Travel_Rarely 483 Research & Development
## 790 790 25 No Travel_Rarely 266 Research & Development
## 457 457 31 No Travel_Rarely 1112 Sales
## 188 188 47 No Travel_Rarely 1225 Sales
## 432 432 40 No Non-Travel 458 Research & Development
## 488 488 31 No Travel_Rarely 670 Research & Development
## 538 538 42 No Travel_Rarely 544 Human Resources
## 752 752 31 Yes Travel_Frequently 1060 Sales
## 215 215 27 No Travel_Rarely 1115 Research & Development
## 540 540 42 No Travel_Rarely 647 Sales
## 613 613 33 Yes Travel_Rarely 465 Research & Development
## 730 730 22 No Travel_Rarely 594 Research & Development
## 296 296 35 No Travel_Rarely 1490 Research & Development
## 328 328 42 No Travel_Rarely 557 Research & Development
## 147 147 33 No Travel_Rarely 931 Research & Development
## 701 701 29 No Travel_Rarely 352 Human Resources
## 708 708 31 No Travel_Rarely 1398 Human Resources
## 84 84 37 No Travel_Rarely 1319 Research & Development
## 83 83 38 No Travel_Frequently 508 Research & Development
## 250 250 41 No Travel_Rarely 1276 Sales
## 767 767 41 No Travel_Rarely 509 Research & Development
## 281 281 43 No Travel_Rarely 1179 Sales
## 675 675 27 No Travel_Rarely 486 Research & Development
## 843 843 52 No Travel_Frequently 890 Research & Development
## 431 431 27 No Travel_Rarely 1377 Sales
## 30 30 52 No Non-Travel 715 Research & Development
## 10 10 34 No Travel_Frequently 653 Research & Development
## 817 817 30 No Travel_Rarely 946 Research & Development
## 441 441 32 No Travel_Frequently 1316 Research & Development
## 820 820 28 Yes Travel_Rarely 1475 Sales
## 345 345 30 No Travel_Rarely 1329 Sales
## 592 592 34 Yes Travel_Rarely 790 Sales
## 585 585 44 No Travel_Rarely 1459 Research & Development
## 660 660 36 No Travel_Frequently 1195 Research & Development
## 12 12 28 No Non-Travel 280 Human Resources
## 293 293 27 No Travel_Rarely 894 Research & Development
## 303 303 55 No Travel_Rarely 1441 Research & Development
## 738 738 59 No Travel_Rarely 326 Sales
## 678 678 40 No Travel_Frequently 692 Research & Development
## 338 338 34 No Travel_Frequently 618 Research & Development
## 350 350 56 No Travel_Rarely 832 Research & Development
## 720 720 28 No Travel_Rarely 950 Research & Development
## 658 658 54 No Travel_Frequently 1050 Research & Development
## 107 107 29 No Non-Travel 1496 Research & Development
## 543 543 27 No Non-Travel 1450 Research & Development
## 518 518 42 No Travel_Rarely 855 Research & Development
## 854 854 28 No Travel_Rarely 1476 Research & Development
## 43 43 49 No Travel_Rarely 464 Research & Development
## 189 189 50 No Travel_Rarely 797 Research & Development
## 171 171 30 No Travel_Rarely 501 Sales
## 39 39 56 No Travel_Rarely 206 Human Resources
## 216 216 40 No Travel_Rarely 896 Research & Development
## 291 291 45 No Travel_Rarely 974 Research & Development
## 58 58 29 No Travel_Rarely 1370 Research & Development
## 395 395 42 No Travel_Frequently 1271 Research & Development
## 856 856 35 No Travel_Rarely 660 Sales
## 456 456 40 No Travel_Rarely 611 Sales
## 22 22 48 No Travel_Rarely 817 Sales
## 170 170 29 No Travel_Rarely 1107 Research & Development
## 588 588 36 Yes Travel_Rarely 1218 Sales
## 63 63 31 No Travel_Rarely 1154 Sales
## 676 676 46 No Travel_Rarely 168 Sales
## 719 719 35 Yes Travel_Frequently 880 Sales
## 417 417 30 No Non-Travel 1116 Research & Development
## 789 789 55 No Travel_Rarely 478 Research & Development
## 70 70 37 No Travel_Rarely 1372 Research & Development
## 59 59 37 No Non-Travel 728 Research & Development
## 800 800 36 No Travel_Rarely 1040 Research & Development
## 484 484 56 No Travel_Rarely 713 Research & Development
## 203 203 32 No Travel_Rarely 859 Research & Development
## 227 227 35 No Travel_Rarely 464 Research & Development
## 243 243 29 No Travel_Rarely 1328 Research & Development
## 413 413 37 No Travel_Rarely 408 Research & Development
## 577 577 38 No Travel_Rarely 1404 Sales
## 542 542 33 No Travel_Rarely 867 Research & Development
## 476 476 46 No Travel_Frequently 1211 Sales
## 744 744 23 No Travel_Rarely 160 Research & Development
## 804 804 35 Yes Travel_Rarely 1204 Sales
## 405 405 35 No Travel_Rarely 1343 Research & Development
## 397 397 46 No Travel_Rarely 1009 Research & Development
## 607 607 59 No Travel_Frequently 1225 Sales
## 501 501 58 Yes Travel_Rarely 601 Research & Development
## 429 429 32 Yes Travel_Rarely 515 Research & Development
## 205 205 32 No Travel_Rarely 1401 Sales
## 475 475 28 No Travel_Rarely 895 Research & Development
## 104 104 23 No Travel_Rarely 885 Research & Development
## 210 210 41 No Travel_Rarely 802 Sales
## 471 471 27 No Travel_Rarely 1354 Research & Development
## 66 66 32 No Travel_Rarely 334 Research & Development
## 88 88 35 Yes Travel_Rarely 622 Research & Development
## 826 826 46 No Travel_Rarely 430 Research & Development
## 601 601 24 No Non-Travel 830 Sales
## 309 309 55 No Travel_Frequently 135 Research & Development
## 737 737 39 No Travel_Rarely 722 Sales
## 294 294 47 No Non-Travel 1162 Research & Development
## 421 421 34 No Travel_Rarely 1153 Research & Development
## 769 769 46 No Travel_Rarely 150 Research & Development
## 430 430 34 No Travel_Rarely 470 Research & Development
## 512 512 39 No Non-Travel 1485 Research & Development
## 361 361 52 No Travel_Rarely 699 Research & Development
## 814 814 47 No Travel_Rarely 1176 Human Resources
## 480 480 37 No Travel_Rarely 161 Research & Development
## 15 15 46 No Travel_Rarely 991 Human Resources
## 486 486 41 Yes Travel_Rarely 1102 Sales
## 254 254 31 Yes Travel_Rarely 359 Human Resources
## 491 491 49 No Travel_Frequently 636 Research & Development
## 713 713 48 No Travel_Rarely 969 Research & Development
## 178 178 26 Yes Travel_Frequently 342 Research & Development
## 428 428 21 No Travel_Rarely 996 Research & Development
## 691 691 38 No Travel_Frequently 888 Human Resources
## 194 194 48 No Travel_Rarely 163 Sales
## 599 599 27 No Travel_Rarely 155 Research & Development
## 802 802 31 No Travel_Rarely 828 Sales
## 378 378 42 No Travel_Rarely 419 Sales
## 639 639 24 No Travel_Rarely 691 Research & Development
## 155 155 34 No Travel_Rarely 546 Research & Development
## 166 166 20 Yes Travel_Rarely 500 Sales
## 207 207 43 Yes Travel_Rarely 1372 Sales
## 468 468 36 No Travel_Rarely 132 Research & Development
## 105 105 53 No Travel_Rarely 447 Research & Development
## 482 482 31 No Travel_Rarely 1222 Research & Development
## 587 587 38 No Non-Travel 573 Research & Development
## 280 280 55 No Travel_Rarely 1229 Research & Development
## 440 440 38 No Travel_Rarely 827 Research & Development
## 389 389 36 No Travel_Rarely 530 Sales
## 499 499 36 No Travel_Rarely 1299 Research & Development
## 251 251 33 No Non-Travel 1313 Research & Development
## 231 231 45 No Travel_Rarely 1329 Research & Development
## 564 564 31 No Non-Travel 697 Research & Development
## 640 640 27 Yes Travel_Rarely 1420 Sales
## 463 463 50 No Travel_Frequently 809 Sales
## 100 100 34 No Travel_Rarely 401 Research & Development
## 731 731 43 No Travel_Rarely 177 Research & Development
## 141 141 56 No Travel_Rarely 1369 Research & Development
## 841 841 18 Yes Travel_Rarely 230 Research & Development
## 745 745 25 No Travel_Rarely 1382 Sales
## 36 36 32 No Travel_Frequently 1311 Research & Development
## 792 792 34 No Travel_Rarely 629 Research & Development
## 362 362 29 Yes Travel_Frequently 746 Sales
## 86 86 59 No Travel_Rarely 715 Research & Development
## 138 138 54 No Travel_Rarely 1441 Research & Development
## 344 344 30 No Travel_Frequently 160 Research & Development
## 211 211 41 No Travel_Rarely 582 Research & Development
## 461 461 42 No Travel_Frequently 1474 Research & Development
## 591 591 22 No Non-Travel 457 Research & Development
## 829 829 38 No Travel_Frequently 594 Research & Development
## 672 672 33 No Travel_Rarely 392 Sales
## 653 653 40 No Travel_Frequently 593 Research & Development
## 832 832 19 Yes Travel_Rarely 528 Sales
## 371 371 31 No Travel_Frequently 853 Research & Development
## 380 380 30 Yes Travel_Rarely 138 Research & Development
## 97 97 46 No Travel_Rarely 228 Sales
## 49 49 18 Yes Travel_Frequently 1306 Sales
## 469 469 26 Yes Non-Travel 265 Sales
## 260 260 25 No Travel_Rarely 977 Research & Development
## 650 650 40 Yes Travel_Rarely 1329 Research & Development
## 174 174 33 No Travel_Rarely 536 Sales
## 775 775 42 No Travel_Frequently 288 Research & Development
## 554 554 48 No Travel_Rarely 1469 Research & Development
## 723 723 34 No Travel_Frequently 560 Research & Development
## 183 183 43 No Travel_Frequently 957 Research & Development
## 347 347 34 No Travel_Frequently 1003 Research & Development
## 74 74 41 No Travel_Rarely 167 Research & Development
## 641 641 30 No Travel_Rarely 1358 Research & Development
## 631 631 55 No Travel_Rarely 836 Research & Development
## 118 118 29 No Travel_Rarely 1378 Research & Development
## 271 271 45 No Non-Travel 589 Sales
## 551 551 41 No Travel_Rarely 314 Human Resources
## 763 763 41 No Non-Travel 256 Sales
## 357 357 26 No Travel_Rarely 841 Research & Development
## 252 252 36 No Non-Travel 1229 Sales
## 619 619 39 No Travel_Rarely 613 Research & Development
## 566 566 44 No Travel_Rarely 1037 Research & Development
## 459 459 56 No Travel_Rarely 1255 Research & Development
## 61 61 36 Yes Travel_Rarely 530 Sales
## 179 179 32 No Travel_Frequently 967 Sales
## 272 272 34 No Travel_Rarely 1130 Research & Development
## 76 76 30 No Travel_Frequently 721 Research & Development
## 87 87 47 No Travel_Rarely 703 Sales
## 224 224 28 No Travel_Rarely 1423 Research & Development
## 739 739 35 No Travel_Rarely 1219 Sales
## 164 164 42 No Travel_Rarely 916 Research & Development
## 13 13 35 No Travel_Rarely 950 Research & Development
## 526 526 53 No Travel_Frequently 124 Sales
## 836 836 37 No Non-Travel 142 Sales
## 693 693 42 No Travel_Rarely 1332 Research & Development
## 545 545 34 No Travel_Rarely 121 Research & Development
## 334 334 27 No Travel_Rarely 608 Research & Development
## 95 95 50 No Travel_Rarely 1452 Research & Development
## 657 657 51 No Travel_Rarely 1469 Research & Development
## 269 269 33 No Travel_Frequently 1303 Research & Development
## 53 53 44 No Travel_Rarely 1117 Research & Development
## 367 367 34 No Travel_Frequently 135 Research & Development
## 420 420 29 No Travel_Rarely 986 Research & Development
## 54 54 45 No Non-Travel 805 Research & Development
## 383 383 48 No Travel_Rarely 1221 Sales
## 571 571 41 No Travel_Rarely 337 Sales
## 274 274 36 No Travel_Rarely 329 Sales
## 773 773 39 No Travel_Frequently 505 Research & Development
## 168 168 33 No Travel_Rarely 922 Research & Development
## 786 786 31 No Travel_Rarely 182 Research & Development
## 546 546 34 No Travel_Rarely 678 Research & Development
## 191 191 30 No Travel_Rarely 1275 Research & Development
## 572 572 21 Yes Travel_Rarely 1427 Research & Development
## 673 673 28 No Travel_Frequently 467 Sales
## 67 67 35 No Travel_Rarely 1017 Research & Development
## 580 580 29 No Travel_Rarely 1329 Research & Development
## 869 869 45 No Travel_Rarely 1457 Research & Development
## 277 277 22 No Travel_Rarely 253 Research & Development
## 828 828 34 No Travel_Rarely 131 Sales
## 34 34 35 No Travel_Rarely 982 Research & Development
## 504 504 29 No Travel_Rarely 1389 Research & Development
## 586 586 40 No Travel_Rarely 905 Research & Development
## 562 562 58 No Travel_Frequently 1216 Research & Development
## 618 618 44 No Travel_Rarely 1488 Sales
## 615 615 30 No Travel_Rarely 231 Sales
## 694 694 30 Yes Travel_Frequently 464 Research & Development
## 127 127 32 Yes Travel_Frequently 1125 Research & Development
## 31 31 32 No Travel_Frequently 689 Sales
## 793 793 38 No Travel_Rarely 397 Research & Development
## 830 830 50 No Travel_Frequently 1246 Human Resources
## 308 308 34 No Non-Travel 697 Research & Development
## 863 863 34 Yes Non-Travel 1362 Sales
## 68 68 23 No Travel_Rarely 571 Research & Development
## 707 707 54 No Travel_Rarely 431 Research & Development
## 807 807 25 No Travel_Rarely 1372 Sales
## 351 351 42 No Travel_Rarely 1396 Research & Development
## 582 582 42 No Travel_Rarely 810 Research & Development
## 11 11 43 No Travel_Rarely 823 Research & Development
## 149 149 24 No Travel_Rarely 477 Research & Development
## 666 666 29 No Travel_Frequently 461 Research & Development
## 857 857 30 Yes Travel_Rarely 1005 Research & Development
## 589 589 37 No Travel_Rarely 1462 Research & Development
## 349 349 44 No Non-Travel 111 Research & Development
## 742 742 31 No Travel_Rarely 688 Sales
## 648 648 26 No Travel_Rarely 390 Research & Development
## 199 199 44 No Travel_Rarely 477 Research & Development
## 624 624 35 No Travel_Rarely 1146 Human Resources
## 565 565 30 No Travel_Rarely 921 Research & Development
## 310 310 25 No Travel_Rarely 810 Sales
## 754 754 46 Yes Travel_Rarely 1254 Sales
## 604 604 29 No Travel_Rarely 1401 Research & Development
## 795 795 49 No Non-Travel 1002 Research & Development
## 519 519 30 No Travel_Rarely 1138 Research & Development
## 295 295 40 No Travel_Rarely 898 Human Resources
## 558 558 34 No Travel_Rarely 254 Research & Development
## 364 364 40 No Travel_Rarely 1416 Research & Development
## 408 408 36 No Travel_Rarely 917 Research & Development
## 567 567 32 No Travel_Rarely 128 Research & Development
## 235 235 43 No Travel_Frequently 394 Sales
## 122 122 21 No Travel_Rarely 501 Sales
## 628 628 38 No Travel_Rarely 343 Research & Development
## 157 157 40 No Travel_Rarely 444 Sales
## 819 819 30 No Travel_Rarely 1427 Research & Development
## 821 821 32 No Travel_Rarely 634 Research & Development
## 120 120 34 No Travel_Rarely 404 Research & Development
## 346 346 39 No Travel_Rarely 119 Sales
## 539 539 24 Yes Travel_Rarely 1448 Sales
## 439 439 50 Yes Travel_Rarely 869 Sales
## 256 256 43 No Non-Travel 343 Research & Development
## 498 498 37 No Travel_Frequently 889 Research & Development
## 679 679 36 No Travel_Frequently 688 Research & Development
## 165 165 35 No Travel_Rarely 1232 Sales
## 352 352 31 No Travel_Rarely 218 Sales
## 198 198 49 No Travel_Rarely 174 Sales
## 353 353 40 No Travel_Frequently 902 Research & Development
## 758 758 49 No Travel_Rarely 1495 Research & Development
## 852 852 30 No Travel_Rarely 125 Research & Development
## 330 330 42 No Travel_Rarely 1147 Human Resources
## 152 152 32 No Travel_Rarely 929 Sales
## 404 404 36 No Travel_Rarely 711 Research & Development
## 384 384 23 No Travel_Rarely 541 Sales
## 454 454 33 No Travel_Rarely 217 Sales
## 418 418 36 No Travel_Frequently 566 Research & Development
## 372 372 30 No Non-Travel 1398 Sales
## 106 106 26 No Travel_Rarely 1384 Research & Development
## 285 285 38 No Travel_Rarely 433 Human Resources
## 23 23 43 No Travel_Frequently 1001 Research & Development
## 399 399 56 No Non-Travel 667 Research & Development
## 465 465 43 Yes Travel_Frequently 807 Research & Development
## 1 1 32 No Travel_Rarely 117 Sales
## 583 583 38 No Travel_Rarely 833 Research & Development
## 241 241 42 No Travel_Rarely 319 Research & Development
## 51 51 38 No Travel_Rarely 362 Research & Development
## 255 255 40 No Non-Travel 218 Research & Development
## 470 470 53 No Travel_Rarely 1084 Research & Development
## 341 341 36 No Travel_Rarely 852 Research & Development
## 45 45 23 No Travel_Rarely 373 Research & Development
## 121 121 36 No Non-Travel 427 Research & Development
## 507 507 33 Yes Travel_Rarely 350 Sales
## 710 710 50 No Non-Travel 881 Research & Development
## 348 348 26 No Travel_Frequently 1479 Research & Development
## 381 381 47 No Travel_Rarely 1482 Research & Development
## 201 201 40 Yes Non-Travel 1479 Sales
## 581 581 25 No Travel_Rarely 882 Research & Development
## 517 517 25 Yes Travel_Rarely 867 Sales
## 419 419 37 No Travel_Rarely 977 Research & Development
## 772 772 46 No Travel_Rarely 526 Sales
## 64 64 18 No Non-Travel 1431 Research & Development
## 223 223 33 No Non-Travel 530 Sales
## 146 146 29 No Non-Travel 746 Sales
## 25 25 33 No Travel_Rarely 1069 Research & Development
## 446 446 47 No Travel_Rarely 465 Research & Development
## 448 448 42 No Travel_Rarely 1128 Research & Development
## 150 150 28 No Travel_Rarely 580 Research & Development
## 782 782 39 No Travel_Rarely 408 Research & Development
## 853 853 29 No Travel_Rarely 153 Research & Development
## 766 766 45 No Travel_Rarely 1015 Research & Development
## 537 537 38 No Travel_Rarely 130 Sales
## 2 2 40 No Travel_Rarely 1308 Research & Development
## 161 161 30 No Travel_Rarely 852 Research & Development
## 75 75 52 Yes Travel_Rarely 1030 Sales
## 318 318 24 No Travel_Rarely 771 Research & Development
## 414 414 32 No Travel_Frequently 1318 Sales
## 681 681 25 No Travel_Rarely 583 Sales
## 186 186 28 No Travel_Rarely 1158 Research & Development
## 135 135 43 No Travel_Frequently 559 Research & Development
## 732 732 39 Yes Travel_Rarely 1162 Sales
## 785 785 47 No Non-Travel 543 Sales
## 770 770 45 No Non-Travel 1050 Sales
## 801 801 33 No Travel_Frequently 1141 Sales
## 779 779 26 No Non-Travel 786 Research & Development
## 547 547 49 No Travel_Rarely 527 Research & Development
## 278 278 43 No Travel_Frequently 185 Research & Development
## 555 555 40 No Non-Travel 1151 Research & Development
## 263 263 33 Yes Travel_Rarely 813 Research & Development
## 438 438 26 No Travel_Rarely 157 Research & Development
## 206 206 44 No Travel_Rarely 661 Research & Development
## 302 302 35 No Travel_Frequently 853 Sales
## 192 192 31 No Travel_Frequently 1125 Sales
## 590 590 35 No Travel_Rarely 1142 Research & Development
## 834 834 19 No Travel_Rarely 645 Research & Development
## 768 768 40 No Non-Travel 1094 Sales
## 733 733 27 Yes Travel_Frequently 1337 Human Resources
## 237 237 42 No Travel_Rarely 300 Research & Development
## 321 321 32 No Non-Travel 1184 Research & Development
## 125 125 29 No Travel_Frequently 442 Sales
## 784 784 33 No Travel_Frequently 1146 Sales
## 286 286 33 No Travel_Rarely 589 Research & Development
## 682 682 43 No Travel_Frequently 313 Research & Development
## 143 143 46 No Travel_Rarely 566 Research & Development
## 275 275 55 No Travel_Rarely 692 Research & Development
## 436 436 27 No Travel_Rarely 1130 Sales
## 156 156 30 No Non-Travel 641 Sales
## 532 532 36 No Travel_Rarely 884 Sales
## 842 842 29 No Travel_Rarely 332 Human Resources
## 747 747 29 Yes Travel_Rarely 350 Human Resources
## 866 866 48 No Travel_Rarely 855 Research & Development
## 113 113 52 No Travel_Rarely 994 Research & Development
## 535 535 29 Yes Travel_Rarely 318 Research & Development
## 596 596 33 No Travel_Rarely 586 Sales
## 162 162 28 Yes Non-Travel 1366 Research & Development
## 825 825 36 No Travel_Rarely 559 Research & Development
## 728 728 34 No Travel_Frequently 878 Research & Development
## 332 332 32 Yes Travel_Rarely 1045 Sales
## 702 702 34 No Travel_Rarely 1400 Sales
## 870 870 35 No Travel_Frequently 138 Research & Development
## 636 636 35 No Travel_Frequently 636 Research & Development
## 777 777 30 No Travel_Rarely 330 Human Resources
## 128 128 45 No Travel_Rarely 556 Research & Development
## 610 610 33 No Non-Travel 1038 Sales
## 818 818 33 No Travel_Rarely 213 Research & Development
## 451 451 33 No Travel_Rarely 501 Research & Development
## 712 712 57 No Travel_Rarely 210 Sales
## 490 490 34 No Travel_Rarely 970 Research & Development
## 222 222 45 Yes Travel_Frequently 306 Sales
## 647 647 18 Yes Non-Travel 247 Research & Development
## 85 85 36 No Travel_Rarely 429 Research & Development
## 806 806 36 No Travel_Rarely 1383 Research & Development
## 516 516 51 No Travel_Rarely 1302 Research & Development
## 637 637 42 No Travel_Rarely 469 Research & Development
## 159 159 43 No Travel_Frequently 1082 Research & Development
## 289 289 36 No Travel_Frequently 1467 Sales
## 635 635 43 No Travel_Rarely 1001 Research & Development
## 464 464 31 No Travel_Frequently 163 Research & Development
## 654 654 48 No Travel_Rarely 530 Sales
## 299 299 31 Yes Non-Travel 335 Research & Development
## 788 788 52 Yes Travel_Rarely 723 Research & Development
## 761 761 29 Yes Travel_Rarely 906 Research & Development
## 664 664 35 No Travel_Rarely 802 Research & Development
## 331 331 49 No Travel_Rarely 809 Research & Development
## 204 204 28 Yes Travel_Rarely 654 Research & Development
## 57 57 42 Yes Travel_Frequently 933 Research & Development
## 29 29 53 Yes Travel_Rarely 1168 Sales
## 734 734 32 Yes Non-Travel 1474 Sales
## 55 55 31 No Travel_Rarely 746 Research & Development
## 862 862 43 No Travel_Rarely 531 Sales
## 279 279 25 Yes Travel_Rarely 240 Sales
## 472 472 29 Yes Travel_Frequently 459 Research & Development
## 190 190 28 No Non-Travel 120 Sales
## 132 132 49 No Travel_Rarely 1313 Sales
## 409 409 53 No Travel_Rarely 1219 Sales
## 557 557 54 No Travel_Rarely 548 Research & Development
## 340 340 27 No Travel_Rarely 1240 Research & Development
## 437 437 28 Yes Travel_Rarely 890 Research & Development
## 844 844 34 No Travel_Rarely 943 Research & Development
## 238 238 32 No Travel_Rarely 646 Research & Development
## 329 329 38 No Travel_Frequently 1394 Research & Development
## 229 229 24 No Travel_Rarely 1353 Sales
## 423 423 31 Yes Travel_Rarely 249 Sales
## 805 805 44 No Non-Travel 489 Research & Development
## 656 656 26 No Travel_Frequently 496 Research & Development
## 435 435 40 No Travel_Rarely 658 Sales
## 492 492 50 No Travel_Rarely 1322 Research & Development
## 187 187 25 No Travel_Frequently 772 Research & Development
## 575 575 26 No Travel_Rarely 703 Sales
## 671 671 37 No Travel_Rarely 397 Research & Development
## 560 560 26 No Travel_Rarely 933 Sales
## 401 401 30 No Travel_Rarely 317 Research & Development
## 176 176 21 No Travel_Rarely 984 Research & Development
## 715 715 36 No Non-Travel 1434 Sales
## 621 621 20 Yes Travel_Frequently 871 Research & Development
## 823 823 34 No Travel_Rarely 704 Sales
## 815 815 38 No Travel_Rarely 395 Sales
## 133 133 42 No Travel_Frequently 1368 Research & Development
## 425 425 28 No Travel_Rarely 736 Sales
## 38 38 36 Yes Travel_Rarely 1456 Sales
## 692 692 47 Yes Non-Travel 666 Research & Development
## 292 292 36 No Non-Travel 1105 Research & Development
## 690 690 38 No Non-Travel 1336 Human Resources
## 833 833 28 No Travel_Rarely 1179 Research & Development
## 503 503 48 Yes Travel_Frequently 708 Sales
## 21 21 36 No Travel_Rarely 913 Research & Development
## 112 112 52 No Travel_Rarely 1053 Research & Development
## 124 124 38 No Travel_Rarely 243 Sales
## 422 422 37 No Non-Travel 1063 Research & Development
## 91 91 38 No Travel_Frequently 1490 Research & Development
## 259 259 34 No Travel_Rarely 1480 Sales
## 453 453 35 Yes Travel_Rarely 303 Sales
## 35 35 32 No Travel_Rarely 1373 Research & Development
## 17 17 32 No Travel_Rarely 498 Research & Development
## 677 677 36 No Travel_Rarely 676 Research & Development
## 415 415 34 Yes Travel_Frequently 296 Sales
## 813 813 24 No Travel_Rarely 1371 Sales
## 32 32 33 No Travel_Rarely 267 Research & Development
## 320 320 19 No Travel_Rarely 265 Research & Development
## 496 496 51 No Travel_Rarely 1318 Sales
## 225 225 38 No Travel_Rarely 1206 Research & Development
## 226 226 30 No Travel_Rarely 911 Research & Development
## 385 385 51 Yes Travel_Rarely 1323 Research & Development
## 700 700 46 No Travel_Rarely 1450 Research & Development
## 411 411 36 No Travel_Rarely 1278 Human Resources
## 822 822 29 No Travel_Rarely 1247 Sales
## 393 393 31 No Non-Travel 976 Research & Development
## 799 799 19 Yes Non-Travel 504 Research & Development
## 324 324 46 No Travel_Rarely 1003 Research & Development
## 267 267 34 No Travel_Frequently 829 Research & Development
## 698 698 36 No Travel_Rarely 1396 Research & Development
## 264 264 29 No Travel_Frequently 1404 Sales
## 110 110 32 No Travel_Rarely 529 Research & Development
## 494 494 35 No Travel_Rarely 1029 Research & Development
## 42 42 45 No Travel_Rarely 954 Sales
## 245 245 29 No Travel_Rarely 718 Research & Development
## 489 489 48 Yes Travel_Rarely 626 Research & Development
## 300 300 28 Yes Travel_Rarely 1157 Research & Development
## 652 652 60 No Travel_Rarely 696 Sales
## 688 688 33 No Travel_Rarely 147 Research & Development
## 94 94 35 No Travel_Rarely 670 Research & Development
## 377 377 36 Yes Travel_Rarely 885 Research & Development
## 467 467 36 No Travel_Rarely 1157 Sales
## 394 394 60 No Travel_Frequently 1499 Sales
## 445 445 46 No Travel_Rarely 734 Research & Development
## 524 524 49 No Travel_Rarely 1245 Research & Development
## 680 680 31 No Travel_Rarely 154 Sales
## 253 253 45 No Travel_Rarely 252 Research & Development
## 177 177 55 Yes Travel_Rarely 725 Research & Development
## 500 500 39 No Non-Travel 1251 Sales
## 506 506 32 No Travel_Rarely 824 Research & Development
## 858 858 31 No Travel_Frequently 798 Research & Development
## 549 549 38 No Travel_Rarely 849 Research & Development
## 674 674 35 Yes Travel_Frequently 130 Research & Development
## 443 443 28 Yes Travel_Rarely 529 Research & Development
## 14 14 30 No Travel_Rarely 202 Sales
## 333 333 44 Yes Travel_Frequently 429 Research & Development
## 663 663 46 No Travel_Rarely 1277 Sales
## 530 530 56 Yes Travel_Rarely 441 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 523 523 45 No Travel_Rarely 1339 Research & Development
## 369 369 23 No Travel_Rarely 977 Research & Development
## 129 129 43 No Travel_Rarely 930 Research & Development
## 633 633 38 No Travel_Rarely 1035 Sales
## 48 48 38 No Travel_Rarely 770 Sales
## 325 325 39 No Travel_Rarely 466 Research & Development
## 845 845 34 No Travel_Rarely 511 Sales
## 7 7 41 No Travel_Rarely 1283 Research & Development
## 847 847 19 No Travel_Rarely 1181 Research & Development
## 716 716 24 Yes Travel_Rarely 240 Human Resources
## 28 28 33 Yes Travel_Rarely 603 Sales
## 230 230 49 No Travel_Rarely 465 Research & Development
## 416 416 35 No Non-Travel 1180 Research & Development
## 373 373 45 No Travel_Frequently 1249 Research & Development
## 809 809 30 No Travel_Rarely 793 Research & Development
## 729 729 26 Yes Travel_Rarely 471 Research & Development
## 812 812 25 No Travel_Rarely 949 Research & Development
## 528 528 31 No Travel_Rarely 1332 Research & Development
## 37 37 35 Yes Travel_Rarely 737 Sales
## 705 705 59 No Travel_Rarely 818 Human Resources
## 354 354 40 No Non-Travel 1142 Research & Development
## 563 563 27 No Travel_Rarely 1134 Research & Development
## 559 559 42 No Travel_Rarely 933 Research & Development
## 525 525 59 No Travel_Rarely 142 Research & Development
## 868 868 47 No Travel_Rarely 571 Sales
## 27 27 38 No Travel_Rarely 1245 Sales
## 755 755 54 No Travel_Rarely 1217 Research & Development
## 594 594 49 No Travel_Rarely 722 Research & Development
## 520 520 56 Yes Travel_Rarely 1162 Research & Development
## 287 287 22 Yes Travel_Rarely 391 Research & Development
## 620 620 35 No Non-Travel 727 Research & Development
## 753 753 50 No Travel_Frequently 1234 Research & Development
## 65 65 24 Yes Travel_Rarely 984 Research & Development
## 154 154 41 No Travel_Rarely 447 Research & Development
## 662 662 34 No Travel_Rarely 1303 Research & Development
## 247 247 31 No Travel_Rarely 1232 Research & Development
## 850 850 26 No Travel_Frequently 1096 Research & Development
## 33 33 46 No Travel_Rarely 488 Sales
## 550 550 45 No Travel_Rarely 788 Human Resources
## 797 797 45 No Travel_Rarely 1385 Research & Development
## 509 509 37 No Travel_Frequently 319 Sales
## 24 24 31 No Travel_Frequently 715 Sales
## 185 185 39 No Travel_Frequently 443 Research & Development
## 513 513 32 No Travel_Frequently 585 Research & Development
## 553 553 36 No Travel_Frequently 1213 Human Resources
## 455 455 48 No Travel_Rarely 1236 Research & Development
## 477 477 37 No Travel_Rarely 342 Sales
## 360 360 36 No Travel_Rarely 1403 Research & Development
## 846 846 28 No Travel_Rarely 1300 Research & Development
## 217 217 38 No Travel_Rarely 1495 Research & Development
## 765 765 34 No Travel_Rarely 829 Human Resources
## 714 714 36 No Travel_Frequently 541 Sales
## 593 593 28 Yes Travel_Frequently 1496 Sales
## 699 699 18 Yes Travel_Frequently 544 Sales
## 762 762 47 No Travel_Rarely 202 Research & Development
## 824 824 31 Yes Travel_Frequently 874 Research & Development
## 407 407 31 No Travel_Rarely 196 Sales
## 391 391 34 No Travel_Rarely 1111 Sales
## 153 153 32 No Travel_Rarely 906 Sales
## 433 433 49 Yes Travel_Frequently 1475 Research & Development
## 82 82 35 No Travel_Rarely 1370 Research & Development
## 831 831 45 No Non-Travel 248 Research & Development
## 232 232 35 No Travel_Rarely 890 Sales
## 796 796 33 Yes Travel_Frequently 1076 Research & Development
## 685 685 28 No Travel_Rarely 440 Research & Development
## 317 317 34 No Travel_Frequently 303 Sales
## 242 242 45 No Travel_Frequently 364 Research & Development
## 290 290 35 No Travel_Rarely 384 Sales
## 119 119 41 No Travel_Rarely 465 Research & Development
## 808 808 22 No Travel_Rarely 1256 Research & Development
## 6 6 27 No Travel_Frequently 294 Research & Development
## 311 311 34 No Travel_Rarely 1351 Research & Development
## 392 392 26 No Travel_Rarely 1443 Sales
## 481 481 29 No Travel_Rarely 136 Research & Development
## 114 114 35 No Non-Travel 1225 Research & Development
## 864 864 47 No Travel_Frequently 217 Sales
## 683 683 36 No Travel_Rarely 1351 Research & Development
## 368 368 40 No Travel_Rarely 1342 Sales
## 622 622 47 No Travel_Frequently 1379 Research & Development
## 314 314 51 No Travel_Rarely 1169 Research & Development
## 306 306 24 No Non-Travel 1269 Research & Development
## 412 412 41 No Travel_Rarely 918 Sales
## 219 219 30 No Travel_Rarely 438 Research & Development
## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
## 284 7 3 Marketing 1 438
## 848 1 3 Medical 1 417
## 101 11 1 Life Sciences 1 1434
## 623 1 4 Life Sciences 1 797
## 645 5 2 Life Sciences 1 1210
## 400 2 1 Medical 1 1496
## 98 2 2 Life Sciences 1 139
## 103 4 4 Marketing 1 523
## 726 1 3 Life Sciences 1 350
## 602 1 4 Technical Degree 1 1574
## 326 4 3 Life Sciences 1 933
## 79 6 3 Medical 1 302
## 270 20 3 Other 1 1084
## 382 25 1 Life Sciences 1 177
## 184 6 3 Life Sciences 1 1525
## 574 8 2 Technical Degree 1 1823
## 4 1 4 Marketing 1 2016
## 661 24 4 Life Sciences 1 772
## 552 8 2 Medical 1 869
## 212 5 2 Marketing 1 1029
## 195 2 1 Medical 1 1407
## 511 10 2 Life Sciences 1 2041
## 479 8 4 Other 1 1409
## 605 9 3 Marketing 1 1752
## 634 3 2 Life Sciences 1 155
## 578 5 3 Life Sciences 1 1471
## 510 12 3 Technical Degree 1 58
## 687 14 1 Other 1 1421
## 424 1 3 Life Sciences 1 1368
## 379 15 1 Life Sciences 1 1202
## 816 9 3 Medical 1 1238
## 108 26 4 Human Resources 1 1973
## 131 5 3 Medical 1 1669
## 343 5 3 Life Sciences 1 1099
## 41 2 3 Medical 1 1824
## 627 4 1 Medical 1 1445
## 740 25 2 Other 1 652
## 298 7 3 Life Sciences 1 147
## 811 9 3 Medical 1 239
## 258 7 4 Marketing 1 1428
## 629 2 1 Life Sciences 1 1096
## 859 9 4 Life Sciences 1 200
## 182 2 2 Life Sciences 1 1355
## 305 2 3 Technical Degree 1 977
## 358 25 5 Technical Degree 1 565
## 696 2 4 Life Sciences 1 925
## 307 9 3 Life Sciences 1 1030
## 827 16 4 Marketing 1 1761
## 221 2 3 Other 1 786
## 736 2 2 Medical 1 1898
## 561 3 2 Medical 1 238
## 313 3 3 Life Sciences 1 1537
## 136 2 2 Medical 1 807
## 794 8 3 Technical Degree 1 83
## 145 2 1 Medical 1 1375
## 123 9 3 Marketing 1 864
## 776 10 1 Medical 1 784
## 234 3 3 Marketing 1 303
## 608 7 2 Medical 1 382
## 495 10 4 Technical Degree 1 1880
## 534 15 4 Medical 1 1955
## 803 10 1 Medical 1 429
## 297 2 1 Life Sciences 1 256
## 208 29 1 Medical 1 282
## 838 21 4 Life Sciences 1 621
## 569 14 2 Medical 1 936
## 522 24 3 Medical 1 1604
## 248 1 4 Medical 1 2013
## 365 2 2 Medical 1 1336
## 665 23 4 Marketing 1 60
## 643 2 1 Medical 1 1074
## 595 15 3 Life Sciences 1 1628
## 434 4 3 Technical Degree 1 1762
## 757 21 1 Life Sciences 1 942
## 760 2 3 Marketing 1 1277
## 218 22 3 Medical 1 1900
## 727 21 2 Medical 1 900
## 508 2 3 Marketing 1 1391
## 276 4 4 Technical Degree 1 1191
## 169 6 2 Technical Degree 1 630
## 71 1 3 Life Sciences 1 1999
## 573 1 2 Life Sciences 1 1927
## 860 15 3 Medical 1 1295
## 485 23 4 Medical 1 165
## 667 1 1 Medical 1 312
## 460 4 4 Life Sciences 1 1435
## 60 24 1 Life Sciences 1 1273
## 449 29 5 Life Sciences 1 79
## 548 16 4 Medical 1 832
## 19 2 4 Medical 1 1755
## 649 1 4 Marketing 1 100
## 638 13 4 Medical 1 1951
## 670 3 3 Life Sciences 1 1250
## 319 21 4 Life Sciences 1 1390
## 116 19 2 Medical 1 904
## 840 10 3 Life Sciences 1 1867
## 102 8 2 Life Sciences 1 1453
## 214 1 2 Life Sciences 1 827
## 390 1 1 Technical Degree 1 478
## 597 25 3 Technical Degree 1 899
## 771 10 3 Marketing 1 446
## 160 26 5 Medical 1 1242
## 77 1 3 Technical Degree 1 1535
## 529 1 3 Medical 1 105
## 126 7 4 Life Sciences 1 1962
## 262 1 4 Technical Degree 1 1522
## 442 9 4 Medical 1 1709
## 642 12 3 Life Sciences 1 1829
## 181 2 3 Life Sciences 1 1182
## 163 8 4 Marketing 1 677
## 474 29 3 Medical 1 192
## 228 5 3 Life Sciences 1 284
## 774 9 3 Medical 1 90
## 646 4 4 Marketing 1 215
## 265 18 1 Life Sciences 1 150
## 427 7 3 Technical Degree 1 1482
## 781 20 2 Marketing 1 248
## 249 22 3 Life Sciences 1 1392
## 810 17 3 Technical Degree 1 233
## 40 20 3 Life Sciences 1 1135
## 541 7 4 Life Sciences 1 1224
## 497 10 2 Life Sciences 1 544
## 697 4 5 Medical 1 1644
## 865 29 3 Technical Degree 1 1465
## 450 26 3 Marketing 1 1612
## 600 1 3 Medical 1 45
## 778 2 1 Life Sciences 1 1190
## 363 2 1 Marketing 1 1038
## 478 29 2 Medical 1 618
## 403 9 4 Life Sciences 1 1856
## 375 9 1 Life Sciences 1 1680
## 335 2 4 Human Resources 1 140
## 598 2 3 Medical 1 521
## 142 23 4 Life Sciences 1 1308
## 444 1 3 Life Sciences 1 1683
## 741 28 3 Life Sciences 1 183
## 659 6 3 Medical 1 466
## 790 1 3 Medical 1 1303
## 457 5 4 Life Sciences 1 1673
## 188 2 4 Life Sciences 1 1676
## 432 16 2 Life Sciences 1 1340
## 488 26 1 Life Sciences 1 16
## 538 2 1 Technical Degree 1 470
## 752 1 3 Life Sciences 1 1331
## 215 3 4 Medical 1 700
## 540 4 4 Marketing 1 1171
## 613 2 2 Life Sciences 1 328
## 730 2 1 Technical Degree 1 169
## 296 11 4 Medical 1 2003
## 328 18 4 Life Sciences 1 1998
## 147 14 3 Medical 1 252
## 701 6 1 Medical 1 1865
## 708 8 2 Medical 1 1461
## 84 6 3 Medical 1 474
## 83 6 4 Life Sciences 1 1997
## 250 2 5 Life Sciences 1 625
## 767 7 2 Technical Degree 1 1085
## 281 2 3 Medical 1 1706
## 675 8 3 Medical 1 1647
## 843 25 4 Medical 1 867
## 431 2 3 Life Sciences 1 437
## 30 19 4 Medical 1 791
## 10 10 4 Technical Degree 1 1597
## 817 2 3 Medical 1 833
## 441 2 2 Life Sciences 1 1235
## 820 13 2 Marketing 1 1933
## 345 29 4 Life Sciences 1 1211
## 592 24 4 Medical 1 1489
## 585 10 4 Other 1 40
## 660 11 3 Life Sciences 1 85
## 12 1 2 Life Sciences 1 1858
## 293 9 3 Medical 1 260
## 303 22 3 Technical Degree 1 1694
## 738 3 3 Life Sciences 1 1254
## 678 11 3 Technical Degree 1 1985
## 338 3 1 Life Sciences 1 1103
## 350 9 3 Medical 1 762
## 720 3 3 Medical 1 1121
## 658 11 4 Medical 1 1520
## 107 1 1 Technical Degree 1 208
## 543 3 3 Medical 1 224
## 518 12 3 Medical 1 1768
## 854 16 2 Medical 1 412
## 43 16 3 Medical 1 1674
## 189 4 1 Life Sciences 1 385
## 171 27 5 Marketing 1 747
## 39 8 4 Life Sciences 1 1338
## 216 2 3 Medical 1 1474
## 291 1 4 Medical 1 996
## 58 3 1 Medical 1 1586
## 395 2 1 Medical 1 875
## 856 7 1 Life Sciences 1 1492
## 456 7 4 Medical 1 1740
## 22 2 1 Marketing 1 712
## 170 28 4 Life Sciences 1 1120
## 588 9 4 Life Sciences 1 27
## 63 2 2 Life Sciences 1 1996
## 676 4 2 Marketing 1 1280
## 719 12 4 Other 1 1667
## 417 2 3 Medical 1 571
## 789 2 3 Medical 1 1770
## 70 1 3 Life Sciences 1 391
## 59 1 4 Medical 1 380
## 800 3 2 Life Sciences 1 1664
## 484 8 3 Life Sciences 1 241
## 203 4 3 Life Sciences 1 830
## 227 4 2 Other 1 53
## 243 2 3 Life Sciences 1 94
## 413 19 2 Life Sciences 1 61
## 577 1 3 Life Sciences 1 1961
## 542 8 4 Life Sciences 1 1798
## 476 5 4 Marketing 1 62
## 744 4 1 Medical 1 1735
## 804 4 3 Technical Degree 1 1100
## 405 27 1 Medical 1 856
## 397 2 3 Life Sciences 1 575
## 607 1 1 Life Sciences 1 91
## 501 7 4 Medical 1 1360
## 429 1 3 Life Sciences 1 331
## 205 4 2 Life Sciences 1 330
## 475 15 2 Life Sciences 1 1102
## 104 4 3 Medical 1 705
## 210 9 1 Life Sciences 1 176
## 471 2 4 Technical Degree 1 1931
## 66 5 2 Life Sciences 1 21
## 88 14 4 Other 1 1010
## 826 1 4 Medical 1 1069
## 601 13 2 Life Sciences 1 1495
## 309 18 4 Medical 1 1034
## 737 24 1 Marketing 1 2056
## 294 1 1 Medical 1 2000
## 421 1 2 Medical 1 110
## 769 2 4 Technical Degree 1 1228
## 430 2 4 Life Sciences 1 339
## 512 25 2 Life Sciences 1 1397
## 361 1 4 Life Sciences 1 259
## 814 26 4 Life Sciences 1 1625
## 480 10 3 Life Sciences 1 2017
## 15 1 2 Life Sciences 1 1314
## 486 1 2 Life Sciences 1 1
## 254 18 5 Human Resources 1 1842
## 491 10 4 Life Sciences 1 396
## 713 2 2 Technical Degree 1 1258
## 178 2 3 Life Sciences 1 1053
## 428 3 2 Medical 1 379
## 691 10 4 Human Resources 1 1563
## 194 2 5 Marketing 1 595
## 599 4 3 Life Sciences 1 2064
## 802 2 1 Life Sciences 1 604
## 378 12 4 Marketing 1 1943
## 639 23 3 Medical 1 639
## 155 10 3 Life Sciences 1 934
## 166 2 3 Medical 1 922
## 207 9 3 Marketing 1 1188
## 468 6 3 Life Sciences 1 97
## 105 2 3 Medical 1 1472
## 482 11 4 Life Sciences 1 895
## 587 6 3 Medical 1 107
## 280 4 4 Life Sciences 1 1501
## 440 1 4 Life Sciences 1 724
## 389 2 4 Life Sciences 1 1710
## 499 27 3 Medical 1 13
## 251 1 2 Medical 1 1994
## 231 2 2 Other 1 1635
## 564 10 3 Medical 1 1979
## 640 2 1 Marketing 1 667
## 463 12 3 Marketing 1 174
## 100 1 3 Life Sciences 1 1447
## 731 8 3 Life Sciences 1 920
## 141 23 3 Life Sciences 1 1373
## 841 3 3 Life Sciences 1 405
## 745 8 2 Other 1 2018
## 36 7 3 Life Sciences 1 359
## 792 27 2 Medical 1 247
## 362 24 3 Technical Degree 1 1928
## 86 2 3 Life Sciences 1 1032
## 138 17 3 Technical Degree 1 1013
## 344 3 3 Medical 1 680
## 211 28 4 Life Sciences 1 2034
## 461 5 2 Other 1 591
## 591 26 2 Other 1 1605
## 829 2 2 Medical 1 1760
## 672 2 4 Medical 1 1670
## 653 9 4 Medical 1 1166
## 832 22 1 Marketing 1 167
## 371 1 1 Life Sciences 1 1011
## 380 22 3 Life Sciences 1 1004
## 97 3 3 Life Sciences 1 1527
## 49 5 3 Marketing 1 614
## 469 29 2 Medical 1 1037
## 260 2 1 Other 1 1992
## 650 7 3 Life Sciences 1 1649
## 174 10 5 Marketing 1 1268
## 775 2 3 Life Sciences 1 1547
## 554 20 4 Medical 1 945
## 723 1 4 Other 1 1431
## 183 28 3 Medical 1 171
## 347 2 2 Life Sciences 1 1140
## 74 12 4 Life Sciences 1 1158
## 641 24 1 Life Sciences 1 11
## 631 2 4 Technical Degree 1 1873
## 118 13 2 Other 1 2053
## 271 2 4 Life Sciences 1 1845
## 551 1 3 Human Resources 1 734
## 763 10 2 Medical 1 1329
## 357 6 3 Other 1 164
## 252 8 4 Technical Degree 1 990
## 619 6 1 Medical 1 2062
## 566 1 3 Medical 1 2020
## 459 1 2 Life Sciences 1 1441
## 61 3 1 Life Sciences 1 967
## 179 8 3 Marketing 1 207
## 272 3 3 Life Sciences 1 1658
## 76 1 2 Medical 1 57
## 87 14 4 Marketing 1 728
## 224 1 3 Life Sciences 1 1506
## 739 18 3 Medical 1 975
## 164 17 2 Life Sciences 1 347
## 13 7 3 Other 1 845
## 526 2 3 Marketing 1 1050
## 836 9 4 Medical 1 626
## 693 2 4 Other 1 477
## 545 2 4 Medical 1 804
## 334 1 2 Life Sciences 1 725
## 95 11 3 Life Sciences 1 226
## 657 8 4 Life Sciences 1 296
## 269 7 2 Life Sciences 1 1970
## 53 2 1 Life Sciences 1 1246
## 367 19 3 Medical 1 1285
## 420 3 4 Medical 1 564
## 54 4 2 Life Sciences 1 972
## 383 7 3 Marketing 1 1466
## 571 8 3 Marketing 1 1909
## 274 16 4 Marketing 1 1436
## 773 2 4 Technical Degree 1 343
## 168 1 5 Medical 1 612
## 786 8 5 Life Sciences 1 1430
## 546 19 3 Life Sciences 1 1701
## 191 28 2 Medical 1 441
## 572 18 1 Other 1 923
## 673 7 3 Life Sciences 1 1507
## 67 6 4 Life Sciences 1 691
## 580 7 3 Life Sciences 1 1260
## 869 7 3 Medical 1 1195
## 277 11 3 Medical 1 511
## 828 2 3 Marketing 1 1281
## 34 1 4 Medical 1 1172
## 504 21 4 Life Sciences 1 20
## 586 19 2 Medical 1 281
## 562 15 4 Life Sciences 1 1837
## 618 1 5 Marketing 1 68
## 615 8 2 Other 1 982
## 694 4 3 Technical Degree 1 514
## 127 16 1 Life Sciences 1 33
## 31 9 2 Medical 1 195
## 793 2 2 Medical 1 1638
## 830 3 3 Medical 1 644
## 308 3 4 Life Sciences 1 1115
## 863 19 3 Marketing 1 502
## 68 12 2 Other 1 1982
## 707 7 4 Medical 1 1830
## 807 18 1 Life Sciences 1 1399
## 351 6 3 Medical 1 1911
## 582 23 5 Life Sciences 1 468
## 11 6 3 Medical 1 1866
## 149 24 3 Medical 1 1173
## 666 1 3 Life Sciences 1 1753
## 857 3 3 Technical Degree 1 297
## 589 11 3 Medical 1 1411
## 349 17 3 Life Sciences 1 1206
## 742 7 3 Life Sciences 1 613
## 648 17 4 Medical 1 1718
## 199 7 4 Medical 1 36
## 624 26 4 Life Sciences 1 2040
## 565 1 3 Life Sciences 1 806
## 310 8 3 Life Sciences 1 707
## 754 10 3 Life Sciences 1 1869
## 604 6 1 Medical 1 1192
## 795 18 4 Life Sciences 1 275
## 519 6 3 Technical Degree 1 1311
## 295 6 2 Medical 1 1550
## 558 1 2 Life Sciences 1 649
## 364 2 2 Medical 1 352
## 408 6 4 Life Sciences 1 1221
## 567 2 1 Technical Degree 1 362
## 235 26 2 Life Sciences 1 158
## 122 5 1 Medical 1 2021
## 628 15 2 Life Sciences 1 461
## 157 2 2 Marketing 1 1986
## 819 2 1 Medical 1 198
## 821 5 4 Other 1 1607
## 120 2 4 Technical Degree 1 1383
## 346 15 4 Marketing 1 1975
## 539 1 1 Technical Degree 1 554
## 439 3 2 Marketing 1 47
## 256 9 3 Life Sciences 1 1813
## 498 9 3 Medical 1 403
## 679 4 2 Life Sciences 1 2025
## 165 16 3 Marketing 1 406
## 352 7 3 Technical Degree 1 416
## 198 8 4 Technical Degree 1 1138
## 353 26 2 Medical 1 1180
## 758 5 4 Technical Degree 1 1473
## 852 9 2 Medical 1 41
## 330 10 3 Human Resources 1 1408
## 152 10 3 Marketing 1 722
## 404 5 4 Life Sciences 1 1651
## 384 2 1 Technical Degree 1 113
## 454 10 4 Marketing 1 1924
## 418 18 4 Life Sciences 1 407
## 372 22 4 Other 1 567
## 106 3 4 Medical 1 1177
## 285 1 3 Human Resources 1 1152
## 23 9 5 Medical 1 663
## 399 1 4 Life Sciences 1 2026
## 465 17 3 Technical Degree 1 1767
## 1 13 4 Life Sciences 1 859
## 583 18 3 Medical 1 1766
## 241 24 3 Medical 1 605
## 51 1 1 Life Sciences 1 662
## 255 8 1 Medical 1 1468
## 470 13 2 Medical 1 250
## 341 5 4 Life Sciences 1 51
## 45 1 2 Life Sciences 1 1270
## 121 8 3 Life Sciences 1 742
## 507 5 3 Marketing 1 485
## 710 2 4 Life Sciences 1 905
## 348 1 3 Life Sciences 1 384
## 381 5 5 Life Sciences 1 447
## 201 24 3 Life Sciences 1 986
## 581 19 1 Medical 1 1358
## 517 19 2 Marketing 1 952
## 419 1 3 Life Sciences 1 1196
## 772 1 2 Marketing 1 244
## 64 14 3 Medical 1 1839
## 223 16 3 Life Sciences 1 1681
## 146 2 3 Life Sciences 1 469
## 25 1 3 Life Sciences 1 969
## 446 1 3 Technical Degree 1 1438
## 448 13 3 Medical 1 1803
## 150 27 3 Medical 1 1622
## 782 2 4 Technical Degree 1 721
## 853 15 2 Life Sciences 1 15
## 766 5 5 Medical 1 1611
## 537 2 2 Marketing 1 1125
## 2 14 3 Medical 1 1128
## 161 1 1 Life Sciences 1 104
## 75 5 3 Life Sciences 1 1319
## 318 1 2 Life Sciences 1 1981
## 414 10 4 Marketing 1 1853
## 681 4 1 Marketing 1 885
## 186 9 3 Medical 1 377
## 135 10 4 Life Sciences 1 448
## 732 3 2 Medical 1 445
## 785 2 4 Marketing 1 1731
## 770 9 4 Life Sciences 1 1117
## 801 1 3 Life Sciences 1 52
## 779 7 3 Medical 1 1693
## 547 8 2 Other 1 944
## 278 10 4 Life Sciences 1 430
## 555 9 5 Life Sciences 1 287
## 263 14 3 Medical 1 325
## 438 1 3 Medical 1 1952
## 206 9 2 Life Sciences 1 913
## 302 18 5 Life Sciences 1 74
## 192 7 4 Marketing 1 1833
## 590 23 4 Medical 1 75
## 834 9 2 Life Sciences 1 1193
## 768 28 3 Other 1 615
## 733 22 3 Human Resources 1 1944
## 237 2 3 Life Sciences 1 2031
## 321 1 3 Life Sciences 1 951
## 125 2 2 Life Sciences 1 388
## 784 25 3 Medical 1 1220
## 286 28 4 Life Sciences 1 1549
## 682 21 3 Medical 1 525
## 143 7 2 Medical 1 1007
## 275 14 4 Medical 1 254
## 436 8 4 Marketing 1 458
## 156 25 2 Technical Degree 1 475
## 532 1 4 Life Sciences 1 1585
## 842 17 3 Other 1 1419
## 747 13 3 Human Resources 1 1844
## 866 4 3 Life Sciences 1 1363
## 113 7 4 Life Sciences 1 1118
## 535 8 4 Other 1 454
## 596 1 3 Medical 1 855
## 162 24 2 Technical Degree 1 1082
## 825 12 4 Life Sciences 1 1614
## 728 10 4 Medical 1 277
## 332 4 4 Medical 1 291
## 702 9 1 Life Sciences 1 1163
## 870 2 3 Medical 1 269
## 636 4 4 Other 1 1185
## 777 1 3 Life Sciences 1 1499
## 128 25 2 Life Sciences 1 1888
## 610 8 1 Life Sciences 1 1044
## 818 7 3 Medical 1 817
## 451 15 2 Medical 1 2009
## 712 29 3 Marketing 1 568
## 490 8 2 Medical 1 757
## 222 26 4 Life Sciences 1 684
## 647 8 1 Medical 1 1156
## 85 2 4 Life Sciences 1 1294
## 806 10 3 Life Sciences 1 1790
## 516 2 3 Medical 1 408
## 637 2 2 Medical 1 1109
## 159 27 3 Life Sciences 1 1126
## 289 11 2 Technical Degree 1 154
## 635 7 3 Life Sciences 1 451
## 464 24 1 Technical Degree 1 1736
## 654 29 1 Medical 1 473
## 299 9 2 Medical 1 991
## 788 8 4 Medical 1 433
## 761 10 3 Life Sciences 1 994
## 664 10 3 Other 1 1028
## 331 1 3 Life Sciences 1 1677
## 204 1 2 Life Sciences 1 741
## 57 19 3 Medical 1 752
## 29 24 4 Life Sciences 1 1968
## 734 11 4 Other 1 631
## 55 8 4 Life Sciences 1 98
## 862 4 4 Marketing 1 1293
## 279 5 3 Marketing 1 142
## 472 24 2 Life Sciences 1 1868
## 190 4 3 Medical 1 129
## 132 11 4 Marketing 1 1757
## 409 2 4 Life Sciences 1 23
## 557 8 4 Life Sciences 1 578
## 340 2 4 Life Sciences 1 54
## 437 2 4 Medical 1 828
## 844 9 3 Life Sciences 1 1344
## 238 9 4 Life Sciences 1 679
## 329 8 3 Medical 1 1937
## 229 3 2 Other 1 128
## 423 6 4 Life Sciences 1 163
## 805 23 3 Medical 1 132
## 656 11 2 Medical 1 390
## 435 10 4 Marketing 1 954
## 492 28 3 Life Sciences 1 1317
## 187 2 1 Life Sciences 1 1653
## 575 28 2 Marketing 1 641
## 671 7 4 Medical 1 102
## 560 1 3 Life Sciences 1 476
## 401 2 3 Life Sciences 1 548
## 176 1 1 Technical Degree 1 1131
## 715 8 4 Life Sciences 1 789
## 621 6 3 Life Sciences 1 137
## 823 28 3 Marketing 1 2035
## 815 9 3 Marketing 1 893
## 133 28 4 Technical Degree 1 551
## 425 26 3 Life Sciences 1 1387
## 38 13 5 Marketing 1 1733
## 692 29 4 Life Sciences 1 376
## 292 24 4 Life Sciences 1 419
## 690 2 3 Human Resources 1 1805
## 833 19 4 Medical 1 1216
## 503 7 2 Medical 1 1691
## 21 9 2 Medical 1 699
## 112 1 2 Life Sciences 1 976
## 124 7 4 Marketing 1 709
## 422 25 5 Medical 1 529
## 91 2 2 Life Sciences 1 556
## 259 4 3 Life Sciences 1 1882
## 453 27 3 Life Sciences 1 1797
## 35 5 4 Life Sciences 1 1966
## 17 3 4 Medical 1 966
## 677 1 3 Other 1 823
## 415 6 2 Marketing 1 555
## 813 10 4 Marketing 1 507
## 32 21 3 Medical 1 1698
## 320 25 3 Life Sciences 1 1269
## 496 26 4 Marketing 1 851
## 225 9 2 Life Sciences 1 1940
## 226 1 2 Medical 1 1989
## 385 4 4 Life Sciences 1 1081
## 700 15 2 Life Sciences 1 1217
## 411 8 3 Life Sciences 1 878
## 822 20 2 Marketing 1 349
## 393 3 2 Medical 1 1948
## 799 10 3 Medical 1 1248
## 324 8 4 Life Sciences 1 1080
## 267 15 3 Medical 1 1485
## 698 5 2 Life Sciences 1 304
## 264 20 3 Technical Degree 1 974
## 110 2 3 Technical Degree 1 2038
## 494 16 3 Life Sciences 1 1529
## 42 2 2 Technical Degree 1 783
## 245 8 1 Medical 1 1150
## 489 1 2 Life Sciences 1 64
## 300 2 4 Medical 1 440
## 652 7 4 Marketing 1 1233
## 688 4 4 Medical 1 305
## 94 10 4 Medical 1 1587
## 377 16 4 Life Sciences 1 1042
## 467 2 4 Life Sciences 1 1556
## 394 28 3 Marketing 1 573
## 445 2 4 Medical 1 1789
## 524 18 4 Life Sciences 1 638
## 680 7 4 Life Sciences 1 941
## 253 2 3 Life Sciences 1 834
## 177 2 3 Medical 1 787
## 500 21 4 Life Sciences 1 1929
## 506 5 2 Life Sciences 1 1555
## 858 7 2 Life Sciences 1 442
## 549 25 2 Life Sciences 1 421
## 674 25 4 Life Sciences 1 881
## 443 2 4 Life Sciences 1 364
## 14 2 1 Technical Degree 1 508
## 333 1 2 Medical 1 1792
## 663 2 3 Life Sciences 1 1732
## 530 14 4 Life Sciences 1 161
## 3 18 2 Life Sciences 1 1412
## 523 7 3 Life Sciences 1 86
## 369 10 3 Technical Degree 1 1592
## 129 6 3 Medical 1 1402
## 633 3 4 Life Sciences 1 1036
## 48 10 4 Marketing 1 1119
## 325 1 1 Life Sciences 1 1026
## 845 3 2 Life Sciences 1 1779
## 7 5 5 Medical 1 1448
## 847 3 1 Medical 1 201
## 716 22 1 Human Resources 1 1714
## 28 9 4 Marketing 1 1157
## 230 6 1 Life Sciences 1 1661
## 416 2 2 Medical 1 1804
## 373 7 3 Life Sciences 1 425
## 809 16 1 Life Sciences 1 1729
## 729 24 3 Technical Degree 1 622
## 812 1 3 Technical Degree 1 1415
## 528 11 2 Medical 1 1251
## 37 10 3 Medical 1 1639
## 705 6 2 Medical 1 321
## 354 8 2 Life Sciences 1 1552
## 563 16 4 Technical Degree 1 1001
## 559 29 3 Life Sciences 1 836
## 525 3 3 Life Sciences 1 309
## 868 14 3 Medical 1 1503
## 27 14 3 Life Sciences 1 1582
## 755 2 4 Technical Degree 1 126
## 594 25 4 Life Sciences 1 1617
## 520 24 2 Life Sciences 1 1907
## 287 7 1 Life Sciences 1 1878
## 620 3 3 Life Sciences 1 704
## 753 20 5 Medical 1 1606
## 65 17 2 Life Sciences 1 1219
## 154 5 3 Life Sciences 1 1814
## 662 2 4 Life Sciences 1 579
## 247 7 4 Medical 1 462
## 850 6 3 Other 1 1918
## 33 2 3 Technical Degree 1 363
## 550 24 4 Medical 1 1049
## 797 20 2 Medical 1 372
## 509 4 4 Marketing 1 311
## 24 2 4 Other 1 1613
## 185 8 1 Life Sciences 1 602
## 513 10 3 Life Sciences 1 1720
## 553 2 1 Human Resources 1 1890
## 455 1 4 Life Sciences 1 664
## 477 16 4 Marketing 1 868
## 360 6 3 Life Sciences 1 373
## 846 17 2 Medical 1 536
## 217 10 3 Medical 1 634
## 765 3 2 Human Resources 1 847
## 714 3 4 Medical 1 481
## 593 1 3 Technical Degree 1 1486
## 699 3 2 Medical 1 1624
## 762 2 2 Other 1 820
## 824 15 3 Medical 1 1160
## 407 29 4 Marketing 1 1784
## 391 8 2 Life Sciences 1 808
## 153 7 3 Life Sciences 1 420
## 433 28 2 Life Sciences 1 1420
## 82 27 4 Life Sciences 1 1728
## 831 23 2 Life Sciences 1 1002
## 232 2 3 Marketing 1 49
## 796 3 3 Life Sciences 1 702
## 685 21 3 Medical 1 221
## 317 2 4 Marketing 1 216
## 242 25 3 Medical 1 1306
## 290 8 4 Life Sciences 1 805
## 119 14 3 Life Sciences 1 227
## 808 19 1 Medical 1 217
## 6 10 2 Life Sciences 1 733
## 311 1 4 Life Sciences 1 1580
## 392 23 3 Marketing 1 72
## 481 1 3 Life Sciences 1 1954
## 114 2 4 Life Sciences 1 771
## 864 3 3 Medical 1 746
## 683 26 4 Life Sciences 1 1682
## 368 9 2 Medical 1 1548
## 622 16 4 Medical 1 987
## 314 7 4 Medical 1 211
## 306 4 1 Life Sciences 1 888
## 412 6 3 Marketing 1 1708
## 219 18 3 Life Sciences 1 194
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 284 4 Male 73 3 2
## 848 3 Male 48 4 3
## 101 2 Male 91 3 1
## 623 2 Female 72 4 1
## 645 1 Male 95 2 1
## 400 2 Male 86 3 3
## 98 3 Male 100 2 2
## 103 3 Female 72 3 2
## 726 1 Female 62 3 2
## 602 4 Male 62 3 2
## 326 2 Female 76 3 1
## 79 4 Female 57 2 3
## 270 3 Male 85 3 2
## 382 3 Female 61 3 1
## 184 2 Female 32 3 3
## 574 2 Male 92 4 2
## 4 3 Female 48 3 3
## 661 1 Male 80 3 2
## 552 2 Male 63 2 1
## 212 4 Male 90 3 5
## 195 1 Male 36 2 1
## 511 1 Female 100 3 2
## 479 3 Female 54 3 1
## 605 2 Female 52 1 1
## 634 1 Female 39 3 1
## 578 3 Male 90 2 1
## 510 2 Female 49 3 5
## 687 3 Female 84 3 3
## 424 4 Female 97 3 1
## 379 2 Female 69 1 1
## 816 1 Male 60 3 1
## 108 3 Male 71 4 5
## 131 1 Male 43 2 2
## 343 4 Male 34 2 3
## 41 2 Male 52 3 4
## 627 4 Female 42 3 2
## 740 1 Male 54 2 2
## 298 1 Male 66 1 2
## 811 3 Male 66 3 2
## 258 4 Female 73 3 2
## 629 4 Male 65 3 3
## 859 3 Male 94 3 1
## 182 2 Female 31 3 2
## 305 4 Male 51 3 1
## 358 3 Female 71 2 1
## 696 4 Female 65 2 1
## 307 1 Male 64 3 1
## 827 1 Male 70 3 3
## 221 4 Male 60 3 2
## 736 1 Female 89 4 2
## 561 4 Male 30 3 1
## 313 4 Female 33 3 1
## 136 3 Female 38 4 2
## 794 3 Female 59 3 3
## 145 2 Female 89 4 2
## 123 4 Male 74 3 3
## 776 1 Male 79 4 1
## 234 4 Female 52 3 2
## 608 1 Female 44 4 2
## 495 2 Female 63 2 2
## 534 3 Female 34 3 2
## 803 3 Male 99 3 4
## 297 4 Male 45 2 2
## 208 2 Male 70 3 2
## 838 4 Male 74 4 2
## 569 3 Female 68 2 2
## 522 3 Male 51 3 1
## 248 2 Male 68 2 1
## 365 4 Female 58 1 5
## 665 2 Male 72 3 2
## 643 3 Male 40 2 4
## 595 1 Female 80 2 3
## 434 4 Female 91 2 1
## 757 4 Female 51 3 2
## 760 1 Female 94 1 5
## 218 4 Female 58 3 4
## 727 3 Female 54 3 1
## 508 2 Male 52 3 3
## 276 4 Female 92 3 5
## 169 4 Male 78 3 2
## 71 4 Male 76 3 1
## 573 3 Male 42 2 2
## 860 1 Female 73 3 3
## 485 4 Female 94 3 3
## 667 2 Female 42 3 3
## 460 1 Male 37 2 2
## 60 3 Male 73 1 1
## 449 4 Female 50 3 2
## 548 1 Male 51 2 2
## 19 4 Male 97 4 1
## 649 3 Male 32 2 2
## 638 4 Male 39 3 3
## 670 4 Male 85 3 4
## 319 1 Male 44 2 2
## 116 1 Male 32 3 3
## 840 4 Male 91 2 5
## 102 1 Male 31 3 3
## 214 4 Female 43 2 2
## 390 1 Female 99 2 1
## 597 4 Male 78 2 3
## 771 2 Male 46 2 2
## 160 1 Female 60 3 3
## 77 4 Male 64 2 3
## 529 2 Male 30 2 1
## 126 3 Female 55 3 2
## 262 1 Male 100 2 1
## 442 4 Female 43 3 1
## 642 2 Female 47 3 2
## 181 3 Male 44 2 3
## 163 3 Male 39 3 2
## 474 3 Male 83 3 1
## 228 4 Male 60 4 1
## 774 4 Male 79 2 1
## 646 3 Male 67 2 3
## 265 2 Male 52 3 1
## 427 3 Male 64 3 1
## 781 2 Female 70 3 1
## 249 4 Female 75 3 2
## 810 3 Male 51 3 1
## 40 3 Male 67 4 1
## 541 3 Male 49 3 2
## 497 1 Male 69 3 1
## 697 3 Male 89 2 4
## 865 2 Male 55 3 3
## 450 1 Male 52 2 2
## 600 2 Male 61 3 1
## 778 3 Female 76 3 1
## 363 1 Female 57 1 5
## 478 1 Male 45 3 2
## 403 2 Female 64 3 1
## 375 4 Male 54 3 1
## 335 3 Female 32 2 5
## 598 2 Female 78 2 2
## 142 1 Male 88 3 1
## 444 3 Female 44 3 1
## 741 1 Male 41 2 2
## 659 4 Male 95 2 2
## 790 4 Female 40 3 1
## 457 1 Female 67 3 2
## 188 2 Female 47 4 4
## 432 3 Male 74 3 1
## 488 1 Male 31 3 1
## 538 3 Male 52 3 1
## 752 4 Female 54 3 1
## 215 1 Male 54 2 1
## 540 2 Male 45 3 2
## 613 1 Female 39 3 1
## 730 3 Male 100 3 1
## 296 4 Male 43 3 1
## 328 4 Male 35 3 2
## 147 4 Female 72 3 1
## 701 4 Male 87 2 1
## 708 4 Female 96 4 1
## 84 3 Male 51 4 2
## 83 1 Male 72 2 2
## 250 2 Female 91 3 4
## 767 2 Female 43 4 1
## 281 4 Male 73 3 2
## 675 2 Female 86 4 1
## 843 3 Female 81 2 4
## 431 4 Male 74 3 2
## 30 4 Male 41 3 1
## 10 4 Male 92 2 2
## 817 3 Female 52 2 2
## 441 4 Female 38 3 2
## 820 4 Female 84 3 2
## 345 3 Male 61 3 2
## 592 1 Female 40 2 2
## 585 4 Male 41 3 2
## 660 2 Male 95 2 2
## 12 3 Male 43 3 1
## 293 4 Female 99 3 1
## 303 1 Male 94 2 1
## 738 3 Female 48 2 2
## 678 4 Female 73 3 2
## 338 1 Male 45 3 2
## 350 3 Male 81 3 4
## 720 4 Female 93 3 3
## 658 2 Female 87 3 4
## 107 4 Male 41 3 2
## 543 3 Male 79 2 1
## 518 2 Male 57 3 1
## 854 2 Male 68 4 2
## 43 4 Female 74 3 1
## 189 1 Male 96 3 5
## 171 3 Male 99 3 2
## 39 4 Male 99 3 5
## 216 3 Male 68 3 1
## 291 4 Female 91 3 1
## 58 2 Male 87 3 1
## 395 2 Male 35 3 1
## 856 4 Male 76 3 1
## 456 2 Male 88 3 5
## 22 2 Male 56 4 2
## 170 3 Female 93 3 1
## 588 3 Male 82 2 1
## 63 1 Male 54 3 1
## 676 4 Female 33 2 5
## 719 4 Male 36 3 2
## 417 3 Female 49 3 1
## 789 3 Male 60 2 5
## 70 4 Female 42 3 1
## 59 1 Female 80 3 3
## 800 4 Male 79 4 2
## 484 3 Female 67 3 1
## 203 3 Female 98 2 2
## 227 3 Male 75 3 1
## 243 3 Male 76 3 1
## 413 2 Male 73 3 1
## 577 1 Male 59 2 1
## 542 4 Male 90 4 1
## 476 1 Male 98 3 2
## 744 3 Female 51 3 1
## 804 4 Male 86 3 3
## 405 3 Female 53 2 1
## 397 1 Male 51 3 4
## 607 1 Female 57 2 2
## 501 3 Female 53 2 3
## 429 4 Male 62 2 1
## 205 3 Female 56 3 1
## 475 1 Male 50 3 1
## 104 1 Male 58 4 1
## 210 3 Male 96 3 3
## 471 2 Male 41 3 1
## 66 1 Male 80 4 1
## 88 3 Male 39 2 1
## 826 4 Male 40 3 5
## 601 4 Female 78 3 1
## 309 3 Male 62 3 2
## 737 2 Female 60 2 4
## 294 3 Female 98 3 3
## 421 1 Male 94 3 2
## 769 4 Male 60 3 2
## 430 4 Male 84 2 2
## 512 3 Male 71 3 3
## 361 3 Male 65 2 5
## 814 4 Female 98 3 5
## 480 3 Female 42 4 3
## 15 4 Female 44 3 1
## 486 2 Female 94 3 2
## 254 4 Male 89 4 1
## 491 3 Female 35 3 5
## 713 4 Male 76 4 1
## 178 1 Male 57 3 1
## 428 4 Male 100 2 1
## 691 3 Male 71 3 2
## 194 2 Female 37 3 2
## 599 2 Male 87 4 2
## 802 2 Male 77 3 2
## 378 2 Male 77 3 2
## 639 2 Male 89 4 1
## 155 2 Male 83 3 1
## 166 3 Female 49 2 1
## 207 1 Female 85 1 2
## 468 2 Female 55 4 1
## 105 4 Male 39 4 4
## 482 4 Male 48 3 1
## 587 2 Female 79 1 2
## 280 4 Male 30 3 2
## 440 2 Female 33 4 2
## 389 3 Female 51 3 2
## 499 3 Male 94 3 2
## 251 2 Male 59 2 1
## 231 4 Female 59 2 2
## 564 3 Female 40 3 3
## 640 3 Male 85 3 1
## 463 3 Female 77 3 3
## 100 4 Female 86 2 1
## 731 1 Female 55 3 2
## 141 4 Male 68 3 4
## 841 3 Male 54 3 1
## 745 1 Female 85 3 2
## 36 2 Male 100 4 1
## 792 4 Female 95 3 1
## 362 3 Male 45 4 1
## 86 3 Female 69 2 4
## 138 3 Female 56 3 3
## 344 3 Female 71 3 1
## 211 1 Female 60 2 4
## 461 2 Male 97 3 1
## 591 2 Female 85 2 1
## 829 3 Female 75 2 1
## 672 4 Male 93 3 2
## 653 2 Female 88 3 3
## 832 4 Male 50 3 1
## 371 3 Female 96 3 2
## 380 1 Female 48 3 1
## 97 3 Female 51 3 4
## 49 2 Male 69 3 1
## 469 2 Male 79 1 2
## 260 4 Male 57 3 1
## 650 1 Male 73 3 1
## 174 4 Male 82 4 3
## 775 4 Male 40 3 3
## 554 4 Male 51 3 1
## 723 4 Male 91 3 1
## 183 2 Female 72 4 1
## 347 4 Male 95 3 2
## 74 2 Male 46 3 1
## 641 4 Male 67 3 1
## 631 2 Male 98 2 1
## 118 4 Male 46 2 2
## 271 3 Female 67 3 2
## 551 4 Male 59 2 5
## 763 3 Male 40 1 2
## 357 3 Female 46 2 1
## 252 1 Male 84 3 2
## 619 4 Male 42 2 3
## 566 2 Male 42 3 1
## 459 1 Female 90 3 1
## 61 3 Male 51 2 3
## 179 2 Female 43 3 3
## 272 4 Female 66 3 2
## 76 3 Female 58 3 2
## 87 4 Male 42 3 2
## 224 1 Male 72 2 1
## 739 3 Female 86 3 2
## 164 4 Female 82 4 2
## 13 3 Male 59 3 3
## 526 3 Female 38 2 3
## 836 1 Male 69 3 3
## 693 1 Male 98 2 2
## 545 3 Female 86 2 1
## 334 3 Female 68 3 3
## 95 3 Female 53 3 5
## 657 2 Male 81 2 3
## 269 4 Male 36 3 2
## 53 1 Female 72 4 1
## 367 3 Female 46 3 2
## 420 2 Male 93 2 3
## 54 3 Male 57 3 2
## 383 3 Male 96 3 2
## 571 3 Female 54 3 2
## 274 3 Female 98 2 2
## 773 3 Female 64 3 3
## 168 1 Female 95 4 4
## 786 1 Female 93 3 4
## 546 2 Female 35 2 1
## 191 4 Female 64 3 2
## 572 4 Female 65 3 1
## 673 3 Male 55 3 2
## 67 2 Male 82 1 2
## 580 3 Male 82 3 2
## 869 1 Female 83 3 1
## 277 1 Female 43 3 1
## 828 3 Female 86 3 2
## 34 4 Male 58 2 1
## 504 2 Female 51 4 3
## 586 3 Male 99 3 2
## 562 1 Male 87 3 4
## 618 2 Female 75 3 2
## 615 3 Male 62 3 3
## 694 3 Male 40 3 1
## 127 2 Female 72 1 1
## 31 4 Male 35 1 2
## 793 4 Female 54 2 3
## 830 1 Male 99 3 5
## 308 3 Male 40 2 1
## 863 1 Male 67 4 2
## 68 4 Male 78 3 1
## 707 4 Female 68 3 2
## 807 1 Male 93 4 2
## 351 3 Male 83 3 3
## 582 1 Female 44 3 4
## 11 1 Female 81 2 5
## 149 4 Male 49 3 1
## 666 4 Male 70 4 2
## 857 4 Female 88 3 1
## 589 1 Female 94 3 1
## 349 4 Male 74 1 1
## 742 3 Male 44 2 3
## 648 4 Male 62 1 1
## 199 1 Female 42 2 3
## 624 3 Female 31 3 3
## 565 4 Male 38 1 1
## 310 4 Male 57 4 2
## 754 3 Female 64 3 3
## 604 2 Female 54 3 1
## 795 4 Male 92 3 2
## 519 1 Female 48 2 2
## 295 3 Male 38 3 4
## 558 2 Male 83 2 1
## 364 1 Male 49 3 5
## 408 3 Male 60 1 1
## 567 4 Male 84 2 2
## 235 3 Male 92 3 4
## 122 3 Male 58 3 1
## 628 3 Male 92 2 3
## 157 2 Female 92 3 2
## 819 2 Male 35 2 1
## 821 2 Female 35 4 1
## 120 3 Female 98 3 2
## 346 2 Male 77 3 4
## 539 1 Female 62 3 1
## 439 1 Male 86 2 1
## 256 1 Male 52 3 1
## 498 2 Male 53 3 1
## 679 4 Female 97 3 2
## 165 3 Male 96 3 3
## 352 2 Male 100 4 2
## 198 4 Male 56 2 4
## 353 3 Female 92 2 2
## 758 1 Male 96 3 2
## 852 4 Male 83 2 1
## 330 3 Female 31 3 4
## 152 4 Male 55 3 2
## 404 2 Female 42 3 3
## 384 3 Male 62 3 1
## 454 2 Male 43 3 2
## 418 3 Male 81 4 1
## 372 3 Female 69 3 3
## 106 1 Male 82 4 1
## 285 3 Male 37 4 1
## 23 4 Male 72 3 2
## 399 3 Male 57 3 2
## 465 3 Male 38 2 1
## 1 2 Male 73 3 2
## 583 2 Male 60 1 2
## 241 4 Male 56 3 3
## 51 3 Female 43 3 1
## 255 4 Male 55 2 3
## 470 4 Female 57 4 2
## 341 2 Female 82 2 1
## 45 4 Male 47 3 1
## 121 1 Female 63 4 3
## 507 4 Female 34 3 1
## 710 1 Male 98 3 4
## 348 3 Female 84 3 2
## 381 4 Male 42 3 5
## 201 2 Female 100 4 4
## 581 4 Male 67 3 1
## 517 3 Male 36 2 1
## 419 4 Female 56 2 2
## 772 2 Female 92 3 3
## 64 2 Female 33 3 1
## 223 3 Female 36 3 2
## 146 4 Male 61 3 2
## 25 2 Female 42 2 2
## 446 1 Male 74 3 1
## 448 2 Male 95 4 2
## 150 2 Female 39 1 2
## 782 4 Female 80 2 2
## 853 4 Female 49 2 2
## 766 3 Female 50 1 2
## 537 4 Male 32 3 3
## 2 3 Male 44 2 5
## 161 4 Male 55 2 2
## 75 2 Male 64 3 3
## 318 2 Male 45 2 2
## 414 4 Male 79 3 2
## 681 3 Male 87 2 2
## 186 4 Male 94 3 1
## 135 3 Female 82 2 2
## 732 4 Female 41 3 2
## 785 3 Male 87 3 2
## 770 2 Female 65 2 2
## 801 3 Female 42 4 2
## 779 4 Male 76 3 1
## 547 1 Female 51 3 3
## 278 3 Female 33 3 1
## 555 4 Male 63 2 2
## 263 3 Male 58 3 1
## 438 3 Male 95 3 1
## 206 2 Male 61 3 1
## 302 2 Male 71 3 3
## 192 1 Female 68 3 3
## 590 3 Female 30 3 1
## 834 3 Male 54 3 1
## 768 3 Male 58 1 3
## 733 1 Female 58 2 1
## 237 1 Male 56 3 5
## 321 3 Female 70 2 1
## 125 2 Male 44 3 2
## 784 2 Female 82 3 2
## 286 2 Male 79 3 2
## 682 4 Male 61 3 1
## 143 4 Male 75 3 3
## 275 3 Male 61 4 5
## 436 2 Female 56 3 2
## 156 4 Female 85 3 2
## 532 2 Female 73 3 2
## 842 2 Male 51 2 3
## 747 1 Male 56 2 1
## 866 4 Male 54 3 3
## 113 2 Male 87 3 3
## 535 2 Male 77 1 1
## 596 1 Male 48 4 2
## 162 2 Male 72 2 3
## 825 3 Female 76 3 2
## 728 4 Male 43 3 1
## 332 4 Male 32 1 3
## 702 2 Female 70 3 2
## 870 2 Female 37 3 2
## 636 4 Male 47 2 1
## 777 3 Male 46 3 1
## 128 2 Female 93 2 2
## 610 2 Female 88 2 1
## 818 3 Male 49 3 3
## 451 2 Female 95 3 2
## 712 1 Male 56 2 4
## 490 2 Female 96 3 2
## 222 1 Female 100 3 2
## 647 3 Male 80 3 1
## 85 3 Female 53 3 2
## 806 4 Male 90 3 3
## 516 4 Male 84 1 2
## 637 4 Male 35 3 4
## 159 3 Female 83 3 3
## 289 2 Female 92 3 3
## 635 3 Female 43 3 3
## 464 4 Female 30 3 2
## 654 1 Female 91 3 3
## 299 3 Male 46 2 1
## 788 3 Male 85 2 2
## 761 4 Female 92 2 1
## 664 2 Male 45 3 1
## 331 3 Male 36 3 4
## 204 1 Female 67 1 1
## 57 3 Male 57 4 1
## 29 1 Male 66 3 3
## 734 4 Male 60 4 2
## 55 3 Female 61 3 2
## 862 4 Female 56 2 3
## 279 3 Male 46 2 2
## 472 4 Male 73 2 1
## 190 2 Male 43 3 2
## 132 4 Female 80 3 2
## 409 1 Female 78 2 4
## 557 3 Female 42 3 2
## 340 4 Female 33 3 1
## 437 3 Male 46 3 1
## 844 4 Male 86 3 3
## 238 1 Female 92 3 2
## 329 4 Female 58 2 2
## 229 1 Female 33 3 2
## 423 2 Male 76 1 2
## 805 2 Male 67 3 2
## 656 1 Male 60 3 2
## 435 1 Male 67 2 3
## 492 4 Female 43 3 4
## 187 4 Male 77 4 2
## 575 1 Male 66 3 2
## 671 1 Male 30 3 3
## 560 3 Male 57 3 2
## 401 3 Female 43 1 2
## 176 4 Female 70 2 1
## 715 1 Male 76 2 3
## 621 4 Female 66 2 1
## 823 4 Female 95 2 2
## 815 2 Male 98 2 1
## 133 4 Female 88 2 2
## 425 3 Male 48 2 2
## 38 2 Male 96 2 2
## 692 1 Male 88 3 3
## 292 2 Female 47 3 2
## 690 1 Male 100 3 1
## 833 4 Male 78 2 1
## 503 4 Female 95 3 1
## 21 2 Male 48 2 2
## 112 4 Male 70 3 4
## 124 4 Female 46 2 2
## 422 2 Female 72 3 2
## 91 4 Male 42 3 1
## 259 3 Male 64 3 3
## 453 3 Male 84 3 2
## 35 4 Male 56 2 2
## 17 3 Female 93 3 2
## 677 3 Female 35 3 2
## 415 4 Female 33 1 1
## 813 4 Female 77 3 2
## 32 2 Male 79 4 1
## 320 2 Female 57 4 1
## 496 1 Female 66 3 4
## 225 2 Male 71 3 1
## 226 4 Male 76 3 1
## 385 1 Male 34 3 1
## 700 4 Male 52 3 5
## 411 1 Male 77 2 1
## 822 4 Male 45 3 2
## 393 3 Male 48 3 1
## 799 1 Female 96 2 1
## 324 4 Female 74 2 2
## 267 2 Male 71 3 4
## 698 4 Male 62 3 2
## 264 3 Female 84 3 1
## 110 4 Male 78 3 1
## 494 4 Female 91 2 3
## 42 2 Male 46 1 2
## 245 2 Male 79 2 2
## 489 1 Male 98 2 3
## 300 1 Male 84 1 1
## 652 2 Male 52 4 2
## 688 3 Female 47 2 1
## 94 1 Female 51 3 2
## 377 3 Female 43 4 1
## 467 3 Male 70 3 1
## 394 3 Female 80 2 3
## 445 3 Male 46 3 5
## 524 4 Male 58 2 5
## 680 2 Male 41 2 1
## 253 2 Female 95 2 1
## 177 4 Male 78 3 5
## 500 1 Female 32 1 2
## 506 4 Female 67 2 2
## 858 3 Female 48 2 3
## 549 1 Female 81 2 3
## 674 4 Female 96 3 1
## 443 1 Male 79 3 1
## 14 3 Male 72 3 1
## 333 3 Male 99 3 1
## 663 3 Male 74 3 3
## 530 2 Female 72 3 1
## 3 3 Male 60 3 3
## 523 2 Male 59 3 3
## 369 4 Male 45 4 1
## 129 1 Female 73 2 2
## 633 2 Male 42 3 2
## 48 3 Male 73 2 3
## 325 4 Female 65 2 4
## 845 4 Female 32 1 2
## 7 2 Male 90 4 1
## 847 2 Female 79 3 1
## 716 4 Male 58 1 1
## 28 1 Female 77 3 2
## 230 3 Female 41 2 4
## 416 2 Male 90 3 2
## 373 1 Male 97 3 3
## 809 2 Male 33 3 1
## 729 3 Male 66 1 1
## 812 1 Male 81 3 1
## 528 3 Male 80 3 2
## 37 4 Male 55 2 3
## 705 2 Male 52 3 1
## 354 4 Male 72 3 2
## 563 3 Female 37 3 1
## 559 2 Male 98 3 2
## 525 3 Male 70 2 1
## 868 3 Female 78 3 2
## 27 3 Male 80 3 2
## 755 1 Female 60 3 3
## 594 3 Female 84 3 1
## 520 1 Male 97 3 1
## 287 4 Male 75 3 1
## 620 3 Male 41 2 1
## 753 2 Male 41 3 4
## 65 4 Female 97 3 1
## 154 2 Male 85 4 2
## 662 4 Male 62 2 1
## 247 3 Female 39 3 3
## 850 3 Male 61 4 1
## 33 3 Female 75 1 4
## 550 2 Male 36 3 1
## 797 3 Male 79 3 4
## 509 1 Male 41 3 1
## 24 4 Male 54 3 2
## 185 3 Female 48 3 1
## 513 1 Male 56 3 1
## 553 2 Male 94 2 2
## 455 4 Female 40 2 4
## 477 4 Male 66 2 2
## 360 4 Male 47 3 1
## 846 3 Male 79 3 2
## 217 3 Female 76 3 2
## 765 3 Male 88 3 1
## 714 1 Male 48 2 3
## 593 1 Male 92 3 1
## 699 2 Female 70 3 1
## 762 3 Female 33 3 4
## 824 3 Male 72 3 1
## 407 1 Female 91 2 2
## 391 3 Female 93 3 2
## 153 4 Male 91 2 2
## 433 1 Male 97 2 2
## 82 4 Male 49 3 2
## 831 4 Male 42 3 2
## 232 4 Female 97 3 1
## 796 1 Male 70 3 1
## 685 3 Male 42 3 1
## 317 3 Female 75 3 1
## 242 2 Female 83 3 5
## 290 1 Female 72 3 1
## 119 1 Male 56 3 1
## 808 3 Male 80 3 1
## 6 4 Male 32 3 3
## 311 2 Male 45 3 2
## 392 3 Female 47 2 2
## 481 1 Male 89 3 2
## 114 4 Female 61 3 2
## 864 4 Female 49 3 4
## 683 1 Male 80 3 2
## 368 1 Male 47 3 2
## 622 3 Male 64 4 2
## 314 2 Male 34 2 2
## 306 1 Male 46 2 1
## 412 4 Male 35 3 3
## 219 1 Female 75 3 1
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 284 Sales Executive 4 Divorced 7547
## 848 Healthcare Representative 4 Divorced 9613
## 101 Laboratory Technician 1 Married 2099
## 623 Research Scientist 4 Single 3280
## 645 Research Scientist 1 Divorced 2107
## 400 Manufacturing Director 3 Single 10209
## 98 Healthcare Representative 4 Divorced 5163
## 103 Sales Executive 3 Married 6538
## 726 Manufacturing Director 3 Married 4898
## 602 Research Scientist 1 Married 4087
## 326 Research Scientist 3 Single 2318
## 79 Sales Executive 4 Single 8865
## 270 Manufacturing Director 1 Married 9957
## 382 Human Resources 3 Married 2942
## 184 Manufacturing Director 2 Single 8376
## 574 Sales Executive 3 Married 6799
## 4 Sales Executive 4 Married 10422
## 661 Laboratory Technician 4 Married 5309
## 552 Human Resources 4 Divorced 4936
## 212 Manager 3 Married 18303
## 195 Research Scientist 4 Single 3904
## 511 Sales Executive 4 Married 5343
## 479 Laboratory Technician 1 Married 2950
## 605 Sales Representative 2 Single 2760
## 634 Laboratory Technician 4 Divorced 2835
## 578 Laboratory Technician 3 Single 3162
## 510 Research Director 3 Married 19545
## 687 Healthcare Representative 4 Single 7553
## 424 Laboratory Technician 4 Single 1611
## 379 Research Scientist 4 Married 2168
## 816 Research Scientist 4 Married 2781
## 108 Manager 2 Married 19636
## 131 Sales Executive 2 Divorced 4187
## 343 Healthcare Representative 4 Divorced 7119
## 41 Manager 2 Divorced 16291
## 627 Sales Executive 3 Married 4162
## 740 Laboratory Technician 4 Single 3681
## 298 Laboratory Technician 3 Single 6074
## 811 Laboratory Technician 1 Divorced 3072
## 258 Sales Executive 1 Divorced 4779
## 629 Manufacturing Director 2 Married 10976
## 859 Laboratory Technician 1 Married 2238
## 182 Sales Executive 1 Divorced 6893
## 305 Research Scientist 3 Single 2479
## 358 Research Scientist 2 Married 2546
## 696 Sales Representative 4 Single 3294
## 307 Laboratory Technician 4 Married 2380
## 827 Sales Executive 3 Married 8161
## 221 Sales Executive 4 Single 6274
## 736 Sales Executive 3 Single 6500
## 561 Laboratory Technician 2 Single 2088
## 313 Research Scientist 3 Single 4084
## 136 Healthcare Representative 2 Married 4244
## 794 Healthcare Representative 3 Divorced 10096
## 145 Healthcare Representative 3 Divorced 6377
## 123 Sales Executive 1 Divorced 10761
## 776 Research Scientist 3 Single 3505
## 234 Sales Executive 1 Married 5940
## 608 Sales Executive 1 Divorced 5605
## 495 Sales Executive 3 Married 5673
## 534 Healthcare Representative 4 Divorced 6667
## 803 Manager 1 Married 17068
## 297 Manufacturing Director 3 Married 9547
## 208 Healthcare Representative 1 Married 6673
## 838 Sales Executive 4 Single 5337
## 569 Sales Executive 3 Single 6244
## 522 Laboratory Technician 2 Married 2408
## 248 Sales Representative 2 Married 2269
## 365 Manager 3 Married 19187
## 665 Sales Executive 3 Single 4568
## 643 Manager 1 Single 16756
## 595 Manufacturing Director 3 Married 10435
## 434 Research Scientist 1 Divorced 2109
## 757 Healthcare Representative 4 Married 4014
## 760 Manager 2 Single 18824
## 218 Manager 4 Divorced 17174
## 727 Sales Representative 4 Married 2973
## 508 Sales Executive 2 Married 10596
## 276 Manager 1 Divorced 19943
## 169 Research Scientist 1 Divorced 5577
## 71 Research Scientist 4 Married 2782
## 573 Sales Executive 4 Married 6834
## 860 Healthcare Representative 4 Married 7978
## 485 Healthcare Representative 4 Married 10312
## 667 Sales Executive 4 Married 7918
## 460 Laboratory Technician 4 Single 5810
## 60 Sales Representative 4 Single 1118
## 449 Laboratory Technician 4 Single 2406
## 548 Laboratory Technician 3 Single 5094
## 19 Research Scientist 4 Married 2932
## 649 Sales Executive 1 Single 4312
## 638 Sales Executive 3 Divorced 8628
## 670 Research Director 4 Married 17779
## 319 Sales Executive 4 Married 6120
## 116 Sales Executive 2 Single 7642
## 840 Research Director 2 Married 19665
## 102 Sales Executive 4 Divorced 10793
## 214 Manufacturing Director 4 Married 6062
## 390 Sales Representative 2 Single 2174
## 597 Manufacturing Director 2 Married 10903
## 771 Sales Executive 4 Single 4682
## 160 Research Director 1 Married 13191
## 77 Sales Executive 3 Married 7083
## 529 Research Scientist 3 Single 2859
## 126 Sales Executive 4 Married 5204
## 262 Research Scientist 1 Single 2362
## 442 Laboratory Technician 3 Married 2974
## 642 Healthcare Representative 1 Divorced 4448
## 181 Manufacturing Director 2 Divorced 10274
## 163 Sales Executive 3 Divorced 7104
## 474 Research Scientist 4 Single 3452
## 228 Research Scientist 2 Divorced 2328
## 774 Research Scientist 3 Married 3388
## 646 Sales Executive 4 Married 10855
## 265 Laboratory Technician 3 Married 2774
## 427 Research Scientist 3 Divorced 1274
## 781 Sales Representative 2 Single 3140
## 249 Research Scientist 4 Divorced 5467
## 810 Research Scientist 2 Married 3058
## 40 Sales Representative 4 Married 2791
## 541 Laboratory Technician 3 Divorced 3491
## 497 Research Scientist 4 Married 2654
## 697 Manager 4 Married 15202
## 865 Manufacturing Director 4 Married 9380
## 450 Sales Executive 1 Married 4385
## 600 Research Scientist 4 Married 2293
## 778 Sales Representative 1 Divorced 2827
## 363 Manager 4 Married 19845
## 478 Sales Executive 3 Divorced 4306
## 403 Laboratory Technician 3 Single 2105
## 375 Sales Representative 1 Single 2728
## 335 Manager 4 Married 18844
## 598 Laboratory Technician 3 Married 3448
## 142 Research Scientist 3 Divorced 2372
## 444 Human Resources 4 Divorced 3195
## 741 Sales Executive 3 Married 8639
## 659 Manufacturing Director 2 Single 6032
## 790 Research Scientist 2 Single 2096
## 457 Sales Executive 4 Married 5476
## 188 Manager 2 Divorced 15972
## 432 Research Scientist 3 Divorced 3544
## 488 Research Scientist 3 Divorced 2911
## 538 Human Resources 3 Divorced 2696
## 752 Sales Representative 2 Single 2302
## 215 Research Scientist 4 Single 2045
## 540 Sales Executive 1 Single 5155
## 613 Laboratory Technician 1 Married 2707
## 730 Laboratory Technician 4 Married 2523
## 296 Laboratory Technician 3 Married 2660
## 328 Research Scientist 1 Divorced 5410
## 147 Research Scientist 2 Married 2756
## 701 Human Resources 2 Married 2804
## 708 Human Resources 2 Single 2109
## 84 Research Scientist 1 Divorced 5974
## 83 Manufacturing Director 3 Married 5321
## 250 Manager 1 Married 16595
## 767 Research Scientist 3 Married 3376
## 281 Sales Executive 4 Married 7847
## 675 Research Scientist 3 Married 3517
## 843 Manufacturing Director 4 Married 13826
## 431 Sales Executive 3 Single 4478
## 30 Research Scientist 4 Married 4258
## 10 Healthcare Representative 3 Married 5063
## 817 Manufacturing Director 4 Single 6877
## 441 Research Scientist 3 Single 4998
## 820 Sales Executive 3 Single 9854
## 345 Sales Executive 1 Divorced 4115
## 592 Sales Executive 2 Single 4599
## 585 Healthcare Representative 4 Married 6465
## 660 Manufacturing Director 2 Single 6499
## 12 Human Resources 4 Divorced 2706
## 293 Research Scientist 2 Single 2279
## 303 Research Scientist 2 Divorced 3537
## 738 Sales Executive 4 Single 5171
## 678 Laboratory Technician 3 Married 6323
## 338 Healthcare Representative 4 Single 7756
## 350 Healthcare Representative 4 Married 11103
## 720 Manufacturing Director 2 Divorced 7655
## 658 Manager 4 Divorced 16032
## 107 Manufacturing Director 3 Married 4319
## 543 Research Scientist 3 Divorced 2566
## 518 Laboratory Technician 2 Divorced 2766
## 854 Healthcare Representative 1 Single 5661
## 43 Laboratory Technician 1 Divorced 2587
## 189 Research Director 2 Divorced 19144
## 171 Sales Executive 4 Divorced 5304
## 39 Manager 2 Single 19717
## 216 Research Scientist 3 Divorced 2345
## 291 Laboratory Technician 4 Divorced 2270
## 58 Laboratory Technician 1 Single 4723
## 395 Research Scientist 4 Single 2515
## 856 Sales Representative 3 Married 2404
## 456 Manager 2 Single 19833
## 22 Sales Executive 2 Married 8120
## 170 Research Scientist 4 Divorced 2514
## 588 Sales Representative 1 Single 3407
## 63 Sales Representative 3 Married 3067
## 676 Manager 2 Married 18789
## 719 Sales Executive 4 Single 4581
## 417 Laboratory Technician 4 Single 2564
## 789 Research Director 1 Married 19038
## 70 Research Scientist 4 Single 2115
## 59 Research Director 4 Divorced 13603
## 800 Healthcare Representative 1 Divorced 6842
## 484 Research Scientist 1 Divorced 4257
## 203 Manufacturing Director 3 Married 6162
## 227 Laboratory Technician 4 Divorced 1951
## 243 Research Scientist 2 Married 2703
## 413 Research Scientist 2 Married 3022
## 577 Sales Representative 1 Single 2858
## 542 Research Scientist 1 Married 3143
## 476 Sales Executive 4 Single 5772
## 744 Laboratory Technician 2 Single 3295
## 804 Sales Executive 1 Single 9582
## 405 Research Scientist 1 Single 2559
## 397 Research Director 3 Married 17861
## 607 Sales Executive 3 Single 5473
## 501 Manufacturing Director 1 Married 10008
## 429 Laboratory Technician 3 Single 3730
## 205 Sales Representative 2 Married 3931
## 475 Laboratory Technician 3 Divorced 2207
## 104 Research Scientist 1 Married 2819
## 210 Sales Executive 3 Divorced 8189
## 471 Research Scientist 2 Married 2226
## 66 Research Scientist 2 Divorced 3298
## 88 Laboratory Technician 2 Divorced 3743
## 826 Research Director 4 Divorced 19627
## 601 Sales Representative 2 Married 2033
## 309 Healthcare Representative 2 Married 6385
## 737 Sales Executive 4 Married 12031
## 294 Research Director 2 Married 11957
## 421 Manufacturing Director 2 Married 4325
## 769 Manufacturing Director 4 Divorced 7379
## 430 Manufacturing Director 1 Married 5957
## 512 Healthcare Representative 3 Married 10920
## 361 Manager 3 Married 19999
## 814 Manager 3 Married 19658
## 480 Research Director 4 Married 13744
## 15 Human Resources 1 Single 3423
## 486 Sales Executive 4 Single 5993
## 254 Human Resources 1 Married 2956
## 491 Research Director 1 Single 18665
## 713 Laboratory Technician 2 Single 2559
## 178 Research Scientist 1 Married 2042
## 428 Research Scientist 3 Single 3230
## 691 Human Resources 3 Married 6077
## 194 Sales Executive 4 Married 4051
## 599 Manufacturing Director 2 Married 6142
## 802 Sales Executive 4 Single 6582
## 378 Sales Executive 4 Divorced 5087
## 639 Research Scientist 4 Married 2725
## 155 Laboratory Technician 2 Divorced 2008
## 166 Sales Representative 3 Single 2044
## 207 Sales Executive 3 Single 5346
## 468 Laboratory Technician 4 Married 3038
## 105 Research Director 2 Single 16598
## 482 Research Scientist 4 Married 2356
## 587 Research Scientist 4 Divorced 5329
## 280 Healthcare Representative 3 Married 4035
## 440 Healthcare Representative 4 Single 7625
## 389 Sales Representative 4 Single 4502
## 499 Healthcare Representative 3 Married 5237
## 251 Laboratory Technician 3 Divorced 2008
## 231 Manufacturing Director 4 Divorced 5770
## 564 Research Director 3 Married 11031
## 640 Sales Representative 1 Divorced 3041
## 463 Sales Executive 4 Single 9208
## 100 Laboratory Technician 2 Married 3294
## 731 Manufacturing Director 2 Divorced 4765
## 141 Manufacturing Director 2 Married 13402
## 841 Laboratory Technician 3 Single 1420
## 745 Sales Executive 3 Divorced 4907
## 36 Laboratory Technician 2 Married 2794
## 792 Research Scientist 2 Single 2311
## 362 Sales Representative 1 Single 1091
## 86 Manufacturing Director 4 Single 13726
## 138 Manufacturing Director 3 Married 10739
## 344 Research Scientist 3 Divorced 2083
## 211 Manufacturing Director 2 Married 13570
## 461 Laboratory Technician 3 Married 2093
## 591 Research Scientist 3 Married 2814
## 829 Laboratory Technician 2 Married 2468
## 672 Sales Executive 4 Divorced 5505
## 653 Research Director 3 Single 13499
## 832 Sales Representative 3 Single 1675
## 371 Manufacturing Director 1 Married 4148
## 380 Research Scientist 3 Married 2132
## 97 Manager 2 Married 16606
## 49 Sales Representative 2 Single 1878
## 469 Sales Executive 1 Single 4969
## 260 Laboratory Technician 3 Divorced 3977
## 650 Laboratory Technician 1 Single 2166
## 174 Sales Executive 3 Divorced 8380
## 775 Healthcare Representative 4 Married 10124
## 554 Research Scientist 3 Married 2259
## 723 Research Scientist 1 Divorced 2996
## 183 Research Scientist 3 Single 4739
## 347 Manufacturing Director 3 Single 4033
## 74 Laboratory Technician 4 Married 4766
## 641 Laboratory Technician 3 Divorced 2693
## 631 Research Scientist 4 Married 2662
## 118 Laboratory Technician 2 Married 4025
## 271 Sales Executive 3 Married 5154
## 551 Manager 3 Married 19189
## 763 Sales Executive 2 Single 6151
## 357 Research Scientist 2 Married 2368
## 252 Sales Executive 4 Divorced 5079
## 619 Healthcare Representative 1 Married 9991
## 566 Research Scientist 4 Single 2436
## 459 Research Scientist 1 Married 2066
## 61 Sales Executive 4 Married 10325
## 179 Sales Executive 4 Single 8998
## 272 Research Scientist 2 Divorced 5433
## 76 Laboratory Technician 4 Single 4011
## 87 Sales Executive 1 Single 4960
## 224 Research Scientist 3 Divorced 1563
## 739 Sales Executive 3 Married 4601
## 164 Research Scientist 1 Single 6545
## 13 Manufacturing Director 3 Single 10221
## 526 Sales Executive 2 Married 7525
## 836 Sales Executive 2 Divorced 8834
## 693 Healthcare Representative 4 Single 6781
## 545 Research Scientist 1 Single 4381
## 334 Manufacturing Director 1 Married 7412
## 95 Manager 2 Single 19926
## 657 Research Director 2 Married 12490
## 269 Healthcare Representative 3 Divorced 5968
## 53 Research Scientist 4 Married 2011
## 367 Laboratory Technician 2 Divorced 4444
## 420 Research Director 3 Married 11935
## 54 Laboratory Technician 2 Married 4447
## 383 Sales Executive 1 Divorced 5486
## 571 Sales Executive 2 Married 4393
## 274 Sales Executive 1 Married 5647
## 773 Healthcare Representative 3 Single 10938
## 168 Research Director 3 Divorced 16184
## 786 Research Director 2 Single 16422
## 546 Research Scientist 4 Married 2929
## 191 Research Scientist 4 Married 5775
## 572 Research Scientist 4 Single 2693
## 673 Sales Executive 1 Single 4898
## 67 Research Scientist 4 Single 6646
## 580 Healthcare Representative 4 Divorced 6623
## 869 Research Scientist 3 Married 4477
## 277 Research Scientist 2 Married 2244
## 828 Sales Executive 1 Single 4538
## 34 Laboratory Technician 3 Married 2258
## 504 Manufacturing Director 1 Divorced 9980
## 586 Laboratory Technician 4 Married 2741
## 562 Research Director 3 Married 15787
## 618 Sales Executive 1 Divorced 5454
## 615 Sales Executive 3 Divorced 7264
## 694 Research Scientist 4 Single 2285
## 127 Research Scientist 1 Single 3919
## 31 Sales Executive 4 Divorced 4668
## 793 Manufacturing Director 3 Married 7756
## 830 Manager 2 Married 18200
## 308 Research Scientist 4 Married 2979
## 863 Sales Executive 4 Single 5304
## 68 Laboratory Technician 4 Single 2647
## 707 Research Scientist 4 Married 6854
## 807 Sales Executive 3 Married 6232
## 351 Research Director 1 Married 13348
## 582 Research Director 4 Single 15992
## 11 Manager 3 Married 19392
## 149 Laboratory Technician 2 Single 3597
## 666 Healthcare Representative 3 Single 6294
## 857 Research Scientist 1 Single 2657
## 589 Laboratory Technician 3 Single 3629
## 349 Research Scientist 3 Single 2290
## 742 Manager 4 Divorced 11557
## 648 Laboratory Technician 3 Married 2305
## 199 Healthcare Representative 4 Married 10248
## 624 Human Resources 4 Single 8837
## 565 Laboratory Technician 3 Married 3833
## 310 Sales Executive 2 Married 4851
## 754 Sales Executive 2 Married 7314
## 604 Laboratory Technician 4 Married 3131
## 795 Manufacturing Director 4 Divorced 6804
## 519 Laboratory Technician 4 Married 4627
## 295 Manager 4 Single 16437
## 558 Research Scientist 4 Married 3622
## 364 Research Director 3 Divorced 19436
## 408 Laboratory Technician 3 Divorced 2741
## 567 Laboratory Technician 1 Single 2176
## 235 Manager 4 Married 16959
## 122 Sales Representative 1 Single 2380
## 628 Research Director 4 Divorced 11510
## 157 Sales Executive 2 Married 5677
## 819 Laboratory Technician 4 Single 2720
## 821 Research Scientist 4 Married 3312
## 120 Healthcare Representative 4 Single 6687
## 346 Sales Executive 1 Single 13341
## 539 Sales Representative 2 Single 3202
## 439 Sales Representative 3 Married 2683
## 256 Research Scientist 3 Single 2438
## 498 Research Scientist 4 Married 2326
## 679 Manufacturing Director 2 Divorced 5131
## 165 Sales Executive 2 Married 8020
## 352 Sales Executive 4 Married 6929
## 198 Sales Executive 2 Married 13120
## 353 Research Scientist 4 Married 4422
## 758 Healthcare Representative 3 Married 6651
## 852 Laboratory Technician 3 Single 2206
## 330 Manager 1 Married 16799
## 152 Sales Executive 4 Single 5396
## 404 Healthcare Representative 1 Married 8008
## 384 Sales Representative 1 Divorced 2322
## 454 Sales Executive 3 Single 5487
## 418 Laboratory Technician 4 Married 3688
## 372 Sales Executive 1 Married 8412
## 106 Laboratory Technician 4 Married 4420
## 285 Human Resources 3 Married 2844
## 23 Laboratory Technician 3 Divorced 5679
## 399 Healthcare Representative 3 Divorced 6306
## 465 Research Scientist 3 Married 2437
## 1 Sales Executive 4 Divorced 4403
## 583 Healthcare Representative 4 Married 5811
## 241 Manufacturing Director 1 Married 7406
## 51 Research Scientist 1 Single 2619
## 255 Research Director 2 Divorced 13757
## 470 Manufacturing Director 1 Divorced 4450
## 341 Research Scientist 1 Married 3419
## 45 Research Scientist 3 Married 1223
## 121 Research Director 1 Married 11713
## 507 Sales Representative 3 Single 2851
## 710 Manager 1 Divorced 17924
## 348 Manufacturing Director 2 Divorced 6397
## 381 Research Director 3 Married 18300
## 201 Sales Executive 2 Single 13194
## 581 Laboratory Technician 4 Married 3669
## 517 Sales Representative 2 Married 2413
## 419 Manufacturing Director 4 Married 6474
## 772 Sales Executive 1 Divorced 10453
## 64 Research Scientist 3 Single 1514
## 223 Sales Executive 4 Divorced 5368
## 146 Sales Executive 3 Married 4649
## 25 Healthcare Representative 4 Single 6949
## 446 Research Scientist 4 Married 3420
## 448 Healthcare Representative 1 Married 5538
## 150 Manufacturing Director 1 Divorced 4877
## 782 Healthcare Representative 3 Single 4553
## 853 Laboratory Technician 3 Single 4193
## 766 Laboratory Technician 1 Single 5769
## 537 Sales Executive 2 Single 7351
## 2 Research Director 3 Single 19626
## 161 Laboratory Technician 4 Married 5126
## 75 Sales Executive 2 Single 8446
## 318 Healthcare Representative 3 Single 4617
## 414 Sales Executive 4 Single 4648
## 681 Sales Executive 1 Married 4256
## 186 Research Scientist 4 Married 2070
## 135 Laboratory Technician 3 Divorced 5257
## 732 Sales Executive 3 Married 5238
## 785 Sales Executive 2 Married 4978
## 770 Sales Executive 3 Married 5593
## 801 Sales Executive 1 Married 5376
## 779 Laboratory Technician 4 Single 2570
## 547 Laboratory Technician 2 Married 7403
## 278 Laboratory Technician 4 Single 2455
## 555 Healthcare Representative 4 Married 4876
## 263 Laboratory Technician 4 Married 2436
## 438 Laboratory Technician 1 Single 2867
## 206 Research Scientist 1 Married 2559
## 302 Sales Executive 1 Married 9069
## 192 Sales Executive 1 Married 9637
## 590 Laboratory Technician 1 Married 4014
## 834 Research Scientist 1 Single 2552
## 768 Sales Executive 1 Divorced 10932
## 733 Human Resources 2 Married 2863
## 237 Manager 3 Married 18880
## 321 Laboratory Technician 2 Married 2332
## 125 Sales Executive 4 Single 4554
## 784 Sales Executive 3 Married 4539
## 286 Laboratory Technician 3 Married 5207
## 682 Laboratory Technician 4 Married 2258
## 143 Manufacturing Director 3 Divorced 10845
## 275 Research Director 2 Single 18722
## 436 Sales Executive 2 Married 6214
## 156 Sales Executive 3 Married 4736
## 532 Sales Executive 3 Single 6815
## 842 Human Resources 1 Single 7988
## 747 Human Resources 1 Divorced 2335
## 866 Manufacturing Director 4 Single 7898
## 113 Healthcare Representative 2 Single 10445
## 535 Laboratory Technician 1 Married 2119
## 596 Sales Executive 1 Divorced 4037
## 162 Healthcare Representative 1 Single 8722
## 825 Manufacturing Director 3 Married 4663
## 728 Research Scientist 3 Divorced 3815
## 332 Sales Executive 4 Married 10400
## 702 Sales Executive 3 Married 5714
## 870 Laboratory Technician 2 Single 4425
## 636 Laboratory Technician 4 Married 2376
## 777 Human Resources 3 Divorced 2064
## 128 Manufacturing Director 4 Married 5906
## 610 Sales Representative 4 Single 2342
## 818 Research Director 3 Married 11691
## 451 Healthcare Representative 4 Married 4878
## 712 Manager 4 Divorced 14118
## 490 Healthcare Representative 3 Single 6142
## 222 Sales Executive 1 Married 4286
## 647 Laboratory Technician 3 Single 1904
## 85 Manufacturing Director 2 Single 5410
## 806 Healthcare Representative 1 Married 8321
## 516 Manufacturing Director 2 Divorced 5482
## 637 Manager 1 Married 17665
## 159 Manufacturing Director 1 Married 10820
## 289 Sales Executive 4 Married 9738
## 635 Healthcare Representative 1 Married 9985
## 464 Manufacturing Director 4 Single 5238
## 654 Manager 3 Married 12504
## 299 Research Scientist 1 Single 2321
## 788 Research Scientist 2 Married 4941
## 761 Research Scientist 1 Single 2404
## 664 Laboratory Technician 4 Divorced 3917
## 331 Manager 3 Single 15379
## 204 Research Scientist 2 Single 2216
## 57 Research Scientist 3 Divorced 2759
## 29 Sales Executive 1 Single 10448
## 734 Sales Executive 3 Married 4707
## 55 Manufacturing Director 4 Single 4424
## 862 Sales Executive 4 Single 10231
## 279 Sales Executive 3 Single 5744
## 472 Research Scientist 4 Single 2439
## 190 Sales Executive 3 Married 4221
## 132 Sales Executive 4 Single 4507
## 409 Manager 4 Married 15427
## 557 Laboratory Technician 3 Single 3780
## 340 Laboratory Technician 1 Divorced 2341
## 437 Research Scientist 3 Single 4382
## 844 Healthcare Representative 4 Divorced 8500
## 238 Research Scientist 4 Married 6322
## 329 Research Scientist 2 Divorced 2133
## 229 Sales Executive 3 Married 4999
## 423 Sales Executive 3 Married 6172
## 805 Laboratory Technician 2 Married 2042
## 656 Healthcare Representative 1 Married 4741
## 435 Sales Executive 2 Divorced 9705
## 492 Research Director 1 Married 16880
## 187 Manufacturing Director 3 Divorced 5206
## 575 Sales Executive 2 Married 6272
## 671 Research Director 3 Single 13664
## 560 Sales Executive 3 Married 5296
## 401 Manufacturing Director 4 Single 6091
## 176 Research Scientist 2 Single 2070
## 715 Sales Executive 1 Single 7587
## 621 Laboratory Technician 4 Single 2926
## 823 Sales Executive 3 Married 6712
## 815 Sales Representative 2 Married 2899
## 133 Healthcare Representative 4 Married 4523
## 425 Sales Executive 1 Married 4724
## 38 Sales Executive 1 Divorced 6134
## 692 Manager 2 Married 11849
## 292 Laboratory Technician 2 Married 5674
## 690 Human Resources 2 Divorced 2592
## 833 Laboratory Technician 1 Married 3196
## 503 Sales Representative 3 Married 2655
## 21 Manufacturing Director 2 Divorced 8847
## 112 Manager 4 Married 17099
## 124 Sales Executive 4 Single 4028
## 422 Research Scientist 3 Married 4449
## 91 Laboratory Technician 4 Married 1702
## 259 Sales Executive 4 Married 9713
## 453 Sales Executive 4 Single 5813
## 35 Manufacturing Director 4 Single 9679
## 17 Manufacturing Director 1 Married 6725
## 677 Manufacturing Director 2 Married 5228
## 415 Sales Representative 3 Divorced 2351
## 813 Sales Executive 3 Divorced 4260
## 32 Laboratory Technician 2 Married 2028
## 320 Research Scientist 4 Single 2994
## 496 Manager 3 Married 16307
## 225 Research Scientist 4 Divorced 4771
## 226 Laboratory Technician 2 Married 3748
## 385 Research Scientist 3 Married 2461
## 700 Research Director 2 Married 19081
## 411 Human Resources 1 Married 2342
## 822 Sales Executive 4 Divorced 6931
## 393 Research Scientist 1 Divorced 3065
## 799 Research Scientist 2 Single 1859
## 324 Research Scientist 1 Divorced 4615
## 267 Research Director 1 Divorced 17007
## 698 Laboratory Technician 2 Single 5914
## 264 Sales Representative 4 Married 2157
## 110 Research Scientist 1 Single 2439
## 494 Healthcare Representative 2 Single 8606
## 42 Sales Representative 3 Single 6632
## 245 Manufacturing Director 4 Married 5056
## 489 Laboratory Technician 3 Single 5381
## 300 Research Scientist 4 Married 3464
## 652 Sales Executive 4 Divorced 5220
## 688 Research Scientist 2 Married 2622
## 94 Healthcare Representative 3 Single 6142
## 377 Laboratory Technician 1 Single 2743
## 467 Sales Representative 4 Single 2644
## 394 Sales Executive 1 Married 10266
## 445 Research Director 4 Divorced 19328
## 524 Research Director 3 Divorced 19502
## 680 Sales Representative 3 Married 2329
## 253 Research Scientist 3 Single 2274
## 177 Manager 1 Married 19859
## 500 Sales Executive 3 Married 5736
## 506 Research Scientist 2 Married 5878
## 858 Manufacturing Director 3 Married 8943
## 549 Research Director 2 Married 12061
## 674 Research Scientist 2 Divorced 2022
## 443 Laboratory Technician 3 Single 3485
## 14 Sales Representative 2 Married 2476
## 333 Research Scientist 2 Divorced 2342
## 663 Sales Executive 4 Divorced 10368
## 530 Research Scientist 2 Married 4963
## 3 Manufacturing Director 4 Single 9362
## 523 Research Scientist 1 Divorced 9724
## 369 Research Scientist 3 Married 2073
## 129 Research Scientist 3 Single 4081
## 633 Sales Executive 4 Single 6861
## 48 Sales Executive 3 Divorced 8740
## 325 Manufacturing Director 4 Married 12742
## 845 Sales Executive 4 Single 6029
## 7 Research Scientist 3 Married 2127
## 847 Laboratory Technician 2 Single 1483
## 716 Human Resources 3 Married 1555
## 28 Sales Executive 1 Single 8224
## 230 Healthcare Representative 3 Married 13966
## 416 Manufacturing Director 4 Divorced 5762
## 373 Laboratory Technician 1 Divorced 5210
## 809 Research Scientist 4 Married 2862
## 729 Laboratory Technician 4 Single 2340
## 812 Laboratory Technician 4 Married 3229
## 528 Healthcare Representative 1 Married 6833
## 37 Sales Executive 1 Married 10306
## 705 Human Resources 3 Married 2267
## 354 Healthcare Representative 4 Divorced 4069
## 563 Laboratory Technician 2 Married 2811
## 559 Manufacturing Director 2 Married 4434
## 525 Research Scientist 4 Married 2177
## 868 Sales Executive 3 Married 4591
## 27 Sales Executive 2 Married 9924
## 755 Research Director 3 Married 13549
## 594 Laboratory Technician 1 Married 3211
## 520 Laboratory Technician 4 Single 2587
## 287 Research Scientist 2 Single 2472
## 620 Laboratory Technician 3 Married 1281
## 753 Healthcare Representative 3 Married 11245
## 65 Laboratory Technician 2 Married 2210
## 154 Healthcare Representative 2 Single 6870
## 662 Research Scientist 3 Divorced 2768
## 247 Manufacturing Director 4 Single 7143
## 850 Laboratory Technician 4 Married 2544
## 33 Manager 2 Married 16872
## 550 Human Resources 2 Single 2177
## 797 Healthcare Representative 4 Married 13496
## 509 Sales Representative 4 Divorced 2793
## 24 Sales Executive 1 Single 5332
## 185 Laboratory Technician 3 Married 3755
## 513 Research Scientist 3 Married 3433
## 553 Human Resources 4 Single 3886
## 455 Manager 1 Married 15402
## 477 Sales Executive 3 Divorced 6334
## 360 Laboratory Technician 4 Married 3210
## 846 Laboratory Technician 1 Divorced 4558
## 217 Healthcare Representative 3 Married 9824
## 765 Human Resources 4 Married 3737
## 714 Sales Executive 4 Married 9699
## 593 Sales Representative 3 Married 2909
## 699 Sales Representative 4 Single 1569
## 762 Manager 4 Married 16752
## 824 Laboratory Technician 3 Married 2610
## 407 Sales Executive 4 Married 5468
## 391 Sales Executive 1 Married 6500
## 153 Sales Executive 3 Married 5484
## 433 Laboratory Technician 1 Single 4284
## 82 Manufacturing Director 3 Married 6883
## 831 Laboratory Technician 1 Married 3633
## 232 Sales Representative 4 Married 2014
## 796 Research Scientist 1 Single 3348
## 685 Research Scientist 4 Married 2713
## 317 Sales Representative 3 Married 2231
## 242 Manager 2 Single 18061
## 290 Sales Representative 4 Married 2572
## 119 Research Scientist 3 Divorced 2451
## 808 Research Scientist 4 Married 2323
## 6 Manufacturing Director 1 Divorced 8793
## 311 Research Scientist 4 Married 5484
## 392 Sales Executive 4 Married 4157
## 481 Healthcare Representative 1 Married 5373
## 114 Healthcare Representative 1 Divorced 5093
## 864 Sales Executive 3 Divorced 13770
## 683 Healthcare Representative 3 Married 5347
## 368 Sales Executive 1 Married 5473
## 622 Manufacturing Director 3 Divorced 5067
## 314 Manufacturing Director 3 Married 6132
## 306 Laboratory Technician 4 Married 3162
## 412 Sales Executive 3 Single 9241
## 219 Research Scientist 3 Single 2632
## MonthlyRate NumCompaniesWorked OverTime PercentSalaryHike PerformanceRating
## 284 7143 4 No 12 3
## 848 10942 0 No 17 3
## 101 7679 0 No 14 3
## 623 13551 2 No 16 3
## 645 20293 6 No 17 3
## 400 19719 5 Yes 18 3
## 98 15850 5 No 14 3
## 103 12740 9 No 15 3
## 726 7505 0 No 12 3
## 602 25174 4 No 14 3
## 326 17808 1 No 19 3
## 79 16840 6 No 12 3
## 270 9096 0 No 15 3
## 382 8916 1 No 23 4
## 184 9150 4 No 18 3
## 574 22128 1 No 21 4
## 4 24032 1 No 19 3
## 661 21146 1 No 15 3
## 552 23965 1 No 13 3
## 212 7770 6 No 13 3
## 195 4050 0 No 12 3
## 511 5982 1 No 11 3
## 479 17363 9 No 13 3
## 605 14630 1 No 13 3
## 634 2561 5 No 22 4
## 578 7973 3 No 14 3
## 510 16280 1 No 12 3
## 687 22930 0 Yes 12 3
## 424 19305 1 No 15 3
## 379 26933 0 Yes 18 3
## 816 6311 0 No 13 3
## 108 25811 4 Yes 18 3
## 131 3356 1 Yes 13 3
## 343 21214 4 No 15 3
## 41 22577 4 No 22 4
## 627 15211 1 Yes 12 3
## 740 14004 4 No 14 3
## 298 22887 1 Yes 24 4
## 811 11012 1 No 11 3
## 258 12761 7 No 14 3
## 629 15813 3 No 18 3
## 859 6961 2 No 21 4
## 182 19461 3 No 15 3
## 305 26227 4 No 24 4
## 358 18300 5 No 16 3
## 696 13137 1 Yes 18 3
## 307 20165 4 No 18 3
## 827 19002 2 No 13 3
## 221 18686 1 No 22 4
## 736 26997 0 No 14 3
## 561 15062 4 No 12 3
## 313 4156 1 No 12 3
## 136 9931 1 No 24 4
## 794 8202 1 No 13 3
## 145 13888 5 No 20 4
## 123 19239 4 Yes 12 3
## 776 19630 1 No 18 3
## 234 17011 2 No 14 3
## 608 19191 1 Yes 24 4
## 495 6060 1 Yes 13 3
## 534 16542 5 No 18 3
## 803 5355 1 Yes 14 3
## 297 14074 1 No 17 3
## 208 11354 7 Yes 19 3
## 838 19921 1 No 12 3
## 569 7824 7 No 17 3
## 522 7324 1 Yes 17 3
## 248 18024 0 No 14 3
## 365 6992 4 No 14 3
## 665 10034 0 No 20 4
## 643 17323 7 No 15 3
## 595 25800 1 No 13 3
## 434 10007 1 No 13 3
## 757 19170 1 Yes 25 4
## 760 2493 2 Yes 16 3
## 218 2437 3 No 11 3
## 727 21222 5 No 15 3
## 508 15395 2 No 11 3
## 276 18575 4 No 13 3
## 169 22087 3 Yes 12 3
## 71 21412 3 No 22 4
## 573 19255 1 Yes 12 3
## 860 14075 1 No 11 3
## 485 3465 1 No 12 3
## 667 6599 1 No 14 3
## 460 22604 1 No 16 3
## 60 8040 1 Yes 14 3
## 449 5456 1 No 11 3
## 548 11983 6 No 14 3
## 19 5586 0 Yes 14 3
## 649 23016 0 No 14 3
## 638 22914 1 No 18 3
## 670 23474 3 No 14 3
## 319 3567 3 Yes 12 3
## 116 4814 1 Yes 13 3
## 840 13583 4 No 12 3
## 102 8386 1 No 18 3
## 214 4051 9 Yes 13 3
## 390 9150 1 Yes 11 3
## 597 9129 3 No 16 3
## 771 4317 3 No 14 3
## 160 23281 3 Yes 17 3
## 77 12288 1 Yes 14 3
## 529 26278 1 No 18 3
## 126 13586 1 Yes 11 3
## 262 7568 6 No 13 3
## 442 25412 9 No 17 3
## 642 10748 2 No 12 3
## 181 19588 2 No 18 3
## 163 20431 0 No 12 3
## 474 9752 5 No 13 3
## 228 12392 1 Yes 16 3
## 774 21777 0 Yes 17 3
## 646 8552 7 No 11 3
## 265 13257 0 No 12 3
## 427 7152 1 No 13 3
## 781 21728 1 Yes 22 4
## 249 13953 3 Yes 14 3
## 810 13364 0 Yes 16 3
## 40 21981 0 No 12 3
## 541 11309 1 No 13 3
## 497 9655 3 No 21 4
## 697 5602 2 No 25 4
## 865 14720 4 Yes 18 3
## 450 24162 1 No 15 3
## 600 3020 2 Yes 16 3
## 778 14947 1 No 12 3
## 363 25846 1 No 15 3
## 478 4267 5 No 12 3
## 403 5411 4 No 12 3
## 375 21082 1 No 11 3
## 335 21922 9 No 21 4
## 598 13436 6 No 22 4
## 142 26076 1 No 12 3
## 444 4167 4 Yes 18 3
## 741 24835 2 No 18 3
## 659 10110 6 Yes 15 3
## 790 18830 1 No 18 3
## 457 22589 1 No 11 3
## 188 21086 6 No 14 3
## 432 8532 9 No 16 3
## 488 15170 1 No 17 3
## 538 24017 0 Yes 11 3
## 752 8319 1 Yes 11 3
## 215 15174 0 No 13 3
## 540 2253 7 No 13 3
## 613 21509 7 No 20 4
## 730 19299 0 No 14 3
## 296 20232 7 Yes 11 3
## 328 11189 6 Yes 17 3
## 147 4673 1 No 13 3
## 701 15434 1 No 11 3
## 708 24609 9 No 18 3
## 84 17001 4 Yes 13 3
## 83 14284 2 No 11 3
## 250 5626 7 No 16 3
## 767 18863 1 No 13 3
## 281 6069 1 Yes 17 3
## 675 22490 7 No 17 3
## 843 19028 3 No 22 4
## 431 5242 1 Yes 11 3
## 30 26589 0 No 18 3
## 10 15332 1 No 14 3
## 817 20234 5 Yes 24 4
## 441 2338 4 Yes 14 3
## 820 23352 3 Yes 11 3
## 345 13192 8 No 19 3
## 592 7815 0 Yes 23 4
## 585 19121 2 Yes 13 3
## 660 22656 1 No 13 3
## 12 10494 1 No 15 3
## 293 11781 1 No 16 3
## 303 23737 5 No 12 3
## 738 16490 5 No 17 3
## 678 26849 1 No 11 3
## 338 22266 0 No 17 3
## 350 20420 7 No 11 3
## 720 8039 0 No 17 3
## 658 24456 3 No 20 4
## 107 26283 1 No 13 3
## 543 25326 1 Yes 15 3
## 518 8952 8 No 22 4
## 854 4824 0 No 19 3
## 43 24941 4 Yes 16 3
## 189 15815 3 No 14 3
## 171 25275 7 No 23 4
## 39 4022 6 No 14 3
## 216 8045 2 No 14 3
## 291 11005 3 No 14 3
## 58 16213 1 Yes 18 3
## 395 9068 5 Yes 14 3
## 856 16192 1 No 13 3
## 456 4349 1 No 14 3
## 22 18597 3 No 12 3
## 170 26968 4 No 22 4
## 588 6986 7 No 23 4
## 63 6393 0 No 19 3
## 676 9946 2 No 14 3
## 719 10414 3 Yes 24 4
## 417 7181 0 No 14 3
## 789 19805 8 No 12 3
## 70 15881 1 No 12 3
## 59 11677 2 Yes 18 3
## 800 26308 6 No 20 4
## 484 13939 4 Yes 18 3
## 203 19124 1 No 12 3
## 227 10910 1 No 12 3
## 243 4956 0 No 23 4
## 413 10227 4 No 21 4
## 577 11473 4 No 14 3
## 542 6076 6 No 19 3
## 476 20445 4 Yes 21 4
## 744 12862 1 No 13 3
## 804 10333 0 Yes 22 4
## 405 17852 1 No 11 3
## 397 2288 6 No 13 3
## 607 24668 7 No 11 3
## 501 12023 7 Yes 14 3
## 429 9571 0 Yes 14 3
## 205 20990 2 No 11 3
## 475 22482 1 No 16 3
## 104 8544 2 No 16 3
## 210 21196 3 Yes 13 3
## 471 6073 1 No 11 3
## 66 15053 0 Yes 12 3
## 88 10074 1 Yes 24 4
## 826 21445 9 No 17 3
## 601 7103 1 No 13 3
## 309 12992 3 Yes 14 3
## 737 8828 0 No 11 3
## 294 17231 0 No 18 3
## 421 17736 1 No 15 3
## 769 17433 2 No 11 3
## 430 23687 6 No 13 3
## 512 3449 3 No 21 4
## 361 5678 0 No 14 3
## 814 5220 3 No 11 3
## 480 15471 1 Yes 25 4
## 15 22957 6 No 12 3
## 486 19479 8 Yes 11 3
## 254 21495 0 No 17 3
## 491 25594 9 Yes 11 3
## 713 16620 5 No 11 3
## 178 15346 6 Yes 14 3
## 428 10531 1 No 17 3
## 691 14814 3 No 11 3
## 194 19658 2 No 14 3
## 599 5174 1 Yes 20 4
## 802 8346 4 Yes 13 3
## 378 2900 3 Yes 12 3
## 639 21630 1 Yes 11 3
## 155 6896 1 No 14 3
## 166 22052 1 No 13 3
## 207 9489 8 No 13 3
## 468 22002 3 No 12 3
## 105 19764 4 No 12 3
## 482 14871 3 Yes 19 3
## 587 15717 7 Yes 12 3
## 280 16143 0 Yes 16 3
## 440 19383 0 No 13 3
## 389 7439 3 No 15 3
## 499 16577 6 No 13 3
## 251 20439 1 No 12 3
## 231 5388 1 No 19 3
## 564 26862 4 No 20 4
## 640 16346 0 No 11 3
## 463 6645 4 No 11 3
## 100 3708 5 No 17 3
## 731 23814 4 No 21 4
## 141 18235 4 Yes 12 3
## 841 25233 1 No 13 3
## 745 13684 0 Yes 22 4
## 36 26062 1 No 20 4
## 792 5711 2 No 15 3
## 362 10642 1 No 17 3
## 86 21829 3 Yes 13 3
## 138 13943 8 No 11 3
## 344 22653 1 No 20 4
## 211 5640 0 No 23 4
## 461 9260 4 No 17 3
## 591 10293 1 Yes 14 3
## 829 15963 4 No 14 3
## 672 3921 1 No 14 3
## 653 13782 9 No 17 3
## 832 26820 1 Yes 19 3
## 371 11275 1 No 12 3
## 380 11539 4 Yes 11 3
## 97 11380 8 No 12 3
## 49 8059 1 Yes 14 3
## 469 21813 8 No 18 3
## 260 7298 6 Yes 19 3
## 650 3339 3 Yes 14 3
## 174 21708 0 Yes 14 3
## 775 18611 2 Yes 14 3
## 554 5543 4 No 17 3
## 723 20284 5 No 14 3
## 183 16090 4 No 12 3
## 347 15834 2 No 11 3
## 74 9051 3 Yes 11 3
## 641 13335 1 No 22 4
## 631 7975 8 No 20 4
## 118 23679 4 Yes 13 3
## 271 19665 4 No 22 4
## 551 19562 1 No 12 3
## 763 22074 1 No 13 3
## 357 23300 1 No 19 3
## 252 25952 4 No 13 3
## 619 21457 4 No 15 3
## 566 13422 6 Yes 12 3
## 459 10494 2 No 22 4
## 61 5518 1 Yes 11 3
## 179 15589 1 No 14 3
## 272 19332 1 No 12 3
## 76 10781 1 No 23 4
## 87 11825 2 No 12 3
## 224 12530 1 No 14 3
## 739 6179 1 No 16 3
## 164 23016 3 Yes 13 3
## 13 18869 3 No 21 4
## 526 23537 2 No 12 3
## 836 24666 1 No 13 3
## 693 17078 3 No 23 4
## 545 7530 1 No 11 3
## 334 6009 1 No 11 3
## 95 17053 3 No 15 3
## 657 15736 5 No 16 3
## 269 18079 1 No 20 4
## 53 19982 1 No 13 3
## 367 22534 4 No 13 3
## 420 21526 1 No 18 3
## 54 23163 1 No 12 3
## 383 24795 4 No 11 3
## 571 26841 5 No 21 4
## 274 13494 4 No 13 3
## 773 6420 0 No 25 4
## 168 22578 4 No 19 3
## 786 8847 3 No 11 3
## 546 20338 1 No 12 3
## 191 11934 1 No 13 3
## 572 8870 1 No 19 3
## 673 11827 0 No 14 3
## 67 19368 1 No 13 3
## 580 4204 1 Yes 11 3
## 869 20100 4 Yes 19 3
## 277 24440 1 No 13 3
## 828 6039 0 Yes 12 3
## 34 16340 6 No 12 3
## 504 10195 1 No 11 3
## 586 16523 8 Yes 15 3
## 562 21624 2 Yes 14 3
## 618 4009 5 Yes 21 4
## 615 9977 5 No 11 3
## 694 3427 9 Yes 23 4
## 127 4681 1 Yes 22 4
## 31 22812 0 No 17 3
## 793 14199 3 Yes 19 3
## 830 7999 1 No 11 3
## 308 22478 3 No 17 3
## 863 4652 8 Yes 13 3
## 68 13672 1 No 13 3
## 707 15696 4 No 15 3
## 807 12477 2 No 11 3
## 351 14842 9 No 13 3
## 582 15901 2 No 14 3
## 11 22539 7 No 13 3
## 149 6409 8 No 22 4
## 666 23060 8 Yes 12 3
## 857 8556 5 Yes 11 3
## 589 19106 4 No 18 3
## 349 4279 2 No 13 3
## 742 25291 9 No 21 4
## 648 6217 1 No 15 3
## 199 2094 3 No 14 3
## 624 16642 1 Yes 16 3
## 565 24375 3 No 21 4
## 310 15678 0 No 22 4
## 754 14011 5 No 21 4
## 604 26342 1 No 13 3
## 795 23793 1 Yes 15 3
## 519 23631 0 No 12 3
## 295 17381 1 Yes 21 4
## 558 22794 1 Yes 13 3
## 364 5949 0 No 19 3
## 408 6865 1 No 14 3
## 567 19737 4 No 13 3
## 235 19494 1 Yes 12 3
## 122 25479 1 Yes 11 3
## 628 15682 0 Yes 14 3
## 157 4258 3 No 14 3
## 819 11162 0 No 13 3
## 821 18783 3 No 17 3
## 120 6163 1 No 11 3
## 346 25098 0 No 12 3
## 539 21972 1 Yes 16 3
## 439 3810 1 Yes 14 3
## 256 24978 4 No 13 3
## 498 11411 1 Yes 12 3
## 679 9192 7 No 13 3
## 165 5100 0 No 15 3
## 352 12241 4 No 11 3
## 198 11879 6 No 17 3
## 353 21203 3 Yes 13 3
## 758 21534 2 No 14 3
## 852 16117 1 No 13 3
## 330 16616 0 No 14 3
## 152 21703 1 No 12 3
## 404 22792 4 No 12 3
## 384 9518 3 No 13 3
## 454 10410 1 No 14 3
## 418 7122 4 No 18 3
## 372 2890 0 No 11 3
## 106 13421 1 No 22 4
## 285 6004 1 No 13 3
## 23 19627 3 Yes 13 3
## 399 26236 1 No 21 4
## 465 15587 9 Yes 16 3
## 1 9250 2 No 11 3
## 583 24539 3 Yes 16 3
## 241 6950 1 Yes 21 4
## 51 14561 3 No 17 3
## 255 25178 2 No 11 3
## 470 26250 1 No 11 3
## 341 13072 9 Yes 14 3
## 45 16901 1 No 22 4
## 121 20335 9 No 14 3
## 507 9150 1 Yes 13 3
## 710 4544 1 No 11 3
## 348 26767 1 No 20 4
## 381 16375 4 No 11 3
## 201 17071 4 Yes 16 3
## 581 9075 3 No 11 3
## 517 18798 1 Yes 18 3
## 419 9961 1 No 13 3
## 772 2137 1 No 25 4
## 64 8018 1 No 16 3
## 223 16130 1 Yes 25 4
## 146 16928 1 No 14 3
## 25 12291 0 No 14 3
## 446 10205 7 No 12 3
## 448 5696 5 No 18 3
## 150 20460 0 No 21 4
## 782 20978 1 No 11 3
## 853 12682 0 Yes 12 3
## 766 23447 1 Yes 14 3
## 537 20619 7 No 16 3
## 2 17544 1 No 14 3
## 161 15998 1 Yes 12 3
## 75 21534 9 Yes 19 3
## 318 14120 1 No 12 3
## 414 26075 8 No 13 3
## 681 18154 1 No 12 3
## 186 2613 1 No 23 4
## 135 6227 1 No 11 3
## 732 17778 4 Yes 18 3
## 785 3536 7 No 11 3
## 770 17970 1 No 13 3
## 801 3193 2 No 19 3
## 779 11925 1 No 20 4
## 547 22477 4 No 11 3
## 278 10675 0 No 19 3
## 555 14242 9 No 14 3
## 263 22149 5 Yes 13 3
## 438 20006 0 No 13 3
## 206 7508 1 Yes 13 3
## 302 11031 1 No 22 4
## 192 8277 2 No 14 3
## 590 16002 3 Yes 15 3
## 834 7172 1 No 25 4
## 768 11373 3 No 15 3
## 733 19555 1 No 12 3
## 237 17312 5 No 11 3
## 321 3974 6 No 20 4
## 125 20260 1 No 18 3
## 784 4905 1 No 12 3
## 286 22949 1 Yes 12 3
## 682 15238 7 No 20 4
## 143 24208 6 No 13 3
## 275 13339 8 No 11 3
## 436 3415 1 No 18 3
## 156 6069 7 Yes 12 3
## 532 21447 6 No 13 3
## 842 9769 1 No 13 3
## 747 3157 4 Yes 15 3
## 866 18706 1 No 11 3
## 113 15322 7 No 19 3
## 535 4759 1 Yes 11 3
## 596 21816 1 No 22 4
## 162 12355 1 No 12 3
## 825 12421 9 Yes 12 3
## 728 5972 1 Yes 17 3
## 332 25812 1 No 11 3
## 702 5829 1 No 20 4
## 870 15986 5 No 11 3
## 636 26537 1 No 13 3
## 777 15428 0 No 21 4
## 128 23888 0 No 13 3
## 610 21437 0 No 19 3
## 818 25995 0 No 11 3
## 451 21653 0 Yes 13 3
## 712 22102 3 No 12 3
## 490 7360 3 No 11 3
## 222 5630 2 No 14 3
## 647 13556 1 No 12 3
## 85 2323 9 Yes 11 3
## 806 25949 7 Yes 13 3
## 516 16321 5 No 18 3
## 637 14399 0 No 17 3
## 159 11535 8 No 11 3
## 289 22952 0 No 14 3
## 635 9262 8 No 16 3
## 464 6670 2 No 20 4
## 654 23978 3 No 21 4
## 299 10322 0 Yes 22 4
## 788 17747 2 No 15 3
## 761 11479 6 Yes 20 4
## 664 9541 1 No 20 4
## 331 22384 4 No 14 3
## 204 3872 7 Yes 13 3
## 57 20366 6 Yes 12 3
## 29 5843 6 Yes 13 3
## 734 23914 8 No 12 3
## 55 20682 1 No 23 4
## 862 20364 3 No 14 3
## 279 26959 1 Yes 11 3
## 472 14753 1 Yes 24 4
## 190 8863 1 No 15 3
## 132 8191 3 No 12 3
## 409 22021 2 No 16 3
## 557 23428 7 No 11 3
## 340 19715 1 No 13 3
## 437 16374 6 No 17 3
## 844 5494 0 No 11 3
## 238 18089 1 Yes 12 3
## 329 18115 1 Yes 16 3
## 229 17519 0 No 21 4
## 423 20739 4 Yes 18 3
## 805 25043 4 No 12 3
## 656 22722 1 Yes 13 3
## 435 20652 2 No 12 3
## 492 22422 4 Yes 11 3
## 187 4973 1 No 17 3
## 575 7428 1 No 20 4
## 671 25258 4 No 13 3
## 560 20156 1 No 17 3
## 401 24793 2 No 20 4
## 176 25326 1 Yes 11 3
## 715 14229 1 No 15 3
## 621 19783 1 Yes 18 3
## 823 8978 1 No 21 4
## 815 12102 0 No 19 3
## 133 4386 0 No 11 3
## 425 24232 1 No 11 3
## 38 8658 5 Yes 13 3
## 692 10268 1 Yes 12 3
## 292 6927 7 No 15 3
## 690 7129 5 No 13 3
## 833 12449 1 No 12 3
## 503 11740 2 Yes 11 3
## 21 13934 2 Yes 11 3
## 112 13829 2 No 15 3
## 124 7791 0 No 20 4
## 422 23866 3 Yes 15 3
## 91 12106 1 Yes 23 4
## 259 24444 2 Yes 13 3
## 453 13492 1 Yes 18 3
## 35 10138 8 No 24 4
## 17 13554 1 No 12 3
## 677 23361 0 No 15 3
## 415 12253 0 No 16 3
## 813 5915 1 Yes 12 3
## 32 13637 1 No 18 3
## 320 21221 1 Yes 12 3
## 496 5594 2 No 14 3
## 225 14293 2 No 19 3
## 226 4077 1 No 13 3
## 385 10332 9 Yes 12 3
## 700 10849 5 No 11 3
## 411 8635 0 No 21 4
## 822 10732 2 No 14 3
## 393 3995 1 Yes 13 3
## 799 6148 1 Yes 25 4
## 324 21029 8 Yes 23 4
## 267 11929 7 No 14 3
## 698 9945 8 No 16 3
## 264 18203 1 No 15 3
## 110 11288 1 No 14 3
## 494 21195 1 No 19 3
## 42 12388 0 No 13 3
## 245 17689 1 Yes 15 3
## 489 19294 9 Yes 13 3
## 300 24737 5 Yes 13 3
## 652 10893 0 Yes 18 3
## 688 13248 6 No 21 4
## 94 4223 3 Yes 16 3
## 377 8269 1 No 16 3
## 467 17001 3 Yes 21 4
## 394 2845 4 No 19 3
## 445 14218 7 Yes 17 3
## 524 2125 1 Yes 17 3
## 680 11737 3 No 15 3
## 253 6153 1 No 14 3
## 177 21199 5 Yes 13 3
## 500 3987 6 No 19 3
## 506 15624 3 No 12 3
## 858 14034 1 No 24 4
## 549 26707 3 No 17 3
## 674 16612 1 Yes 19 3
## 443 14935 2 No 11 3
## 14 17434 1 No 18 3
## 333 11092 1 Yes 12 3
## 663 5596 4 Yes 12 3
## 530 4510 9 Yes 18 3
## 3 19944 2 No 11 3
## 523 18787 2 No 17 3
## 369 12826 2 No 16 3
## 129 20003 1 Yes 14 3
## 633 4981 8 Yes 12 3
## 48 5569 0 Yes 14 3
## 325 7060 1 No 16 3
## 845 25353 5 No 12 3
## 7 5561 2 Yes 12 3
## 847 16102 1 No 14 3
## 716 11585 1 No 11 3
## 28 18385 0 Yes 17 3
## 230 11652 2 Yes 19 3
## 416 24442 2 No 14 3
## 373 20308 1 No 18 3
## 809 3811 1 No 12 3
## 729 23213 1 Yes 18 3
## 812 4910 4 No 11 3
## 528 17089 1 Yes 12 3
## 37 21530 9 No 17 3
## 705 25657 8 No 17 3
## 354 8841 3 Yes 18 3
## 563 12086 9 No 14 3
## 559 11806 1 No 13 3
## 525 8456 3 No 17 3
## 868 24200 3 Yes 17 3
## 27 12355 0 No 11 3
## 755 24001 9 No 12 3
## 594 22102 1 No 14 3
## 520 10261 1 No 16 3
## 287 26092 1 Yes 23 4
## 620 16900 1 No 18 3
## 753 20689 2 Yes 15 3
## 65 3372 1 No 13 3
## 154 15530 3 No 12 3
## 662 8416 3 No 12 3
## 247 25713 1 Yes 14 3
## 850 7102 0 No 18 3
## 33 14977 3 Yes 12 3
## 550 8318 1 No 16 3
## 797 7501 0 Yes 14 3
## 509 2539 4 No 17 3
## 24 21602 7 No 13 3
## 185 17872 1 No 11 3
## 513 17360 6 No 13 3
## 553 4223 1 No 21 4
## 455 17997 7 No 11 3
## 477 24558 4 No 19 3
## 360 20251 0 No 11 3
## 846 13535 1 No 12 3
## 217 22174 3 No 19 3
## 765 2243 0 No 19 3
## 714 7246 4 No 11 3
## 593 15747 3 No 15 3
## 699 18420 1 Yes 12 3
## 762 12982 1 Yes 11 3
## 824 6233 1 No 12 3
## 407 13402 1 No 14 3
## 391 13305 5 No 17 3
## 153 16985 1 No 14 3
## 433 22710 3 No 20 4
## 82 5151 2 No 16 3
## 831 14039 1 Yes 15 3
## 232 9687 1 No 13 3
## 796 3164 1 Yes 11 3
## 685 6672 1 No 11 3
## 317 11314 6 No 18 3
## 242 13035 3 No 22 4
## 290 20317 1 No 16 3
## 119 4609 4 No 12 3
## 808 11992 1 No 24 4
## 6 4809 1 No 21 4
## 311 13008 9 No 17 3
## 392 21436 7 Yes 19 3
## 481 6225 0 No 12 3
## 114 4761 2 No 11 3
## 864 10225 9 Yes 12 3
## 683 7419 6 No 14 3
## 368 19345 0 No 12 3
## 622 6759 1 Yes 19 3
## 314 13983 2 No 17 3
## 306 10778 0 No 17 3
## 412 15869 1 No 12 3
## 219 23910 1 No 14 3
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## 284 4 80 3 13
## 848 1 80 3 19
## 101 2 80 0 6
## 623 3 80 0 10
## 645 1 80 1 5
## 400 2 80 0 16
## 98 4 80 1 17
## 103 1 80 1 6
## 726 4 80 2 5
## 602 2 80 1 9
## 326 3 80 0 1
## 79 4 80 0 23
## 270 3 80 1 7
## 382 4 80 1 8
## 184 4 80 0 9
## 574 3 80 2 10
## 4 3 80 2 14
## 661 4 80 2 10
## 552 4 80 1 6
## 212 2 80 0 21
## 195 4 80 0 5
## 511 3 80 1 10
## 479 3 80 0 12
## 605 3 80 0 2
## 634 1 80 1 7
## 578 4 80 0 7
## 510 4 80 0 23
## 687 1 80 0 9
## 424 3 80 0 0
## 379 1 80 1 6
## 816 2 80 1 15
## 108 1 80 1 35
## 131 2 80 1 10
## 343 3 80 1 9
## 41 4 80 1 37
## 627 3 80 2 5
## 740 4 80 0 9
## 298 4 80 0 9
## 811 3 80 2 12
## 258 2 80 2 8
## 629 2 80 1 23
## 859 4 80 1 7
## 182 4 80 1 11
## 305 1 80 0 7
## 358 2 80 0 6
## 696 1 80 0 3
## 307 2 80 0 8
## 827 1 80 3 10
## 221 3 80 0 6
## 736 2 80 0 9
## 561 3 80 0 13
## 313 1 80 0 7
## 136 4 80 1 8
## 794 2 80 3 17
## 145 2 80 3 15
## 123 3 80 1 18
## 776 4 80 0 2
## 234 4 80 1 16
## 608 3 80 1 8
## 495 1 80 1 10
## 534 2 80 1 9
## 803 4 80 0 21
## 297 3 80 0 10
## 208 2 80 0 17
## 838 4 80 0 10
## 569 1 80 0 10
## 522 3 80 3 1
## 248 2 80 1 3
## 365 4 80 1 23
## 665 3 80 0 10
## 643 2 80 0 31
## 595 4 80 2 18
## 434 3 80 1 1
## 757 4 80 1 10
## 760 1 80 0 26
## 218 2 80 1 24
## 727 2 80 1 10
## 508 2 80 0 14
## 276 4 80 1 28
## 169 2 80 2 18
## 71 1 80 1 12
## 573 3 80 1 7
## 860 4 80 1 10
## 485 4 80 1 40
## 667 4 80 1 11
## 460 3 80 0 10
## 60 4 80 0 1
## 449 4 80 0 10
## 548 4 80 0 10
## 19 1 80 3 6
## 649 2 80 0 16
## 638 3 80 1 9
## 670 1 80 0 36
## 319 4 80 2 8
## 116 4 80 0 10
## 840 4 80 0 29
## 102 1 80 1 13
## 214 4 80 1 8
## 390 3 80 0 3
## 597 1 80 0 16
## 771 3 80 0 9
## 160 3 80 0 20
## 77 4 80 0 10
## 529 1 80 0 6
## 126 4 80 0 10
## 262 3 80 0 11
## 442 3 80 1 9
## 642 2 80 1 15
## 181 2 80 1 15
## 163 4 80 0 6
## 474 2 80 0 9
## 228 1 80 1 4
## 774 1 80 1 2
## 646 1 80 1 15
## 265 3 80 1 6
## 427 2 80 2 1
## 781 4 80 0 4
## 249 1 80 2 12
## 810 4 80 1 6
## 40 1 80 1 3
## 541 1 80 3 10
## 497 4 80 2 8
## 697 2 80 1 23
## 865 4 80 2 10
## 450 1 80 1 10
## 600 1 80 1 6
## 778 3 80 3 1
## 363 4 80 1 33
## 478 1 80 2 8
## 403 3 80 0 7
## 375 1 80 0 2
## 335 4 80 1 30
## 598 2 80 1 20
## 142 4 80 2 2
## 444 1 80 3 8
## 741 4 80 0 6
## 659 4 80 0 8
## 790 4 80 0 2
## 457 1 80 2 10
## 188 3 80 3 29
## 432 2 80 1 6
## 488 4 80 1 5
## 538 3 80 1 4
## 752 1 80 0 3
## 215 4 80 0 5
## 540 4 80 0 9
## 613 1 80 0 13
## 730 3 80 1 3
## 296 3 80 1 5
## 328 3 80 1 9
## 147 4 80 1 8
## 701 4 80 0 1
## 708 4 80 0 8
## 84 1 80 2 13
## 83 4 80 1 10
## 250 2 80 1 22
## 767 3 80 0 10
## 281 1 80 1 10
## 675 1 80 0 5
## 843 3 80 0 31
## 431 1 80 0 5
## 30 1 80 1 5
## 10 2 80 1 8
## 817 2 80 0 12
## 441 4 80 0 10
## 820 4 80 0 6
## 345 3 80 3 8
## 592 3 80 0 16
## 585 4 80 0 9
## 660 3 80 0 6
## 12 2 80 1 3
## 293 4 80 0 7
## 303 4 80 1 8
## 738 4 80 0 13
## 678 1 80 1 10
## 338 3 80 0 7
## 350 3 80 0 30
## 720 2 80 3 10
## 658 1 80 1 26
## 107 1 80 1 10
## 543 4 80 1 1
## 518 2 80 3 7
## 854 3 80 0 9
## 43 2 80 1 17
## 189 1 80 2 28
## 171 4 80 1 10
## 39 1 80 0 36
## 216 3 80 1 8
## 291 4 80 2 8
## 58 4 80 0 10
## 395 4 80 0 8
## 856 1 80 1 1
## 456 2 80 0 21
## 22 4 80 0 12
## 170 1 80 1 11
## 588 2 80 0 10
## 63 3 80 1 3
## 676 3 80 1 26
## 719 1 80 0 13
## 417 3 80 0 12
## 789 2 80 3 34
## 70 2 80 0 17
## 59 1 80 2 15
## 800 1 80 1 13
## 484 3 80 1 19
## 203 3 80 1 14
## 227 3 80 1 1
## 243 4 80 1 6
## 413 1 80 0 8
## 577 1 80 0 20
## 542 2 80 1 14
## 476 3 80 0 14
## 744 3 80 0 3
## 804 1 80 0 9
## 405 4 80 0 6
## 397 3 80 0 26
## 607 4 80 0 20
## 501 4 80 0 31
## 429 4 80 0 4
## 205 1 80 1 6
## 475 4 80 1 4
## 104 1 80 1 5
## 210 3 80 1 12
## 471 3 80 1 6
## 66 4 80 2 7
## 88 4 80 1 5
## 826 4 80 2 23
## 601 3 80 1 1
## 309 4 80 2 17
## 737 1 80 1 21
## 294 1 80 2 14
## 421 3 80 0 5
## 769 3 80 1 12
## 430 2 80 1 13
## 512 2 80 1 13
## 361 1 80 1 34
## 814 3 80 1 27
## 480 1 80 1 16
## 15 3 80 0 10
## 486 1 80 0 8
## 254 3 80 0 2
## 491 4 80 0 22
## 713 3 80 0 7
## 178 2 80 1 6
## 428 1 80 0 3
## 691 3 80 0 10
## 194 1 80 1 14
## 599 2 80 1 6
## 802 3 80 0 10
## 378 3 80 2 14
## 639 2 80 2 6
## 155 2 80 2 1
## 166 4 80 0 2
## 207 2 80 0 7
## 468 2 80 0 5
## 105 2 80 0 35
## 482 2 80 1 8
## 587 4 80 3 17
## 280 2 80 0 4
## 440 3 80 0 10
## 389 3 80 0 17
## 499 2 80 2 17
## 251 3 80 3 1
## 231 1 80 2 10
## 564 3 80 1 13
## 640 2 80 1 5
## 463 4 80 0 16
## 100 1 80 1 7
## 731 3 80 1 4
## 141 1 80 1 33
## 841 3 80 0 0
## 745 2 80 1 6
## 36 3 80 0 5
## 792 4 80 0 9
## 362 4 80 0 1
## 86 1 80 0 30
## 138 3 80 1 22
## 344 3 80 1 1
## 211 3 80 1 21
## 461 4 80 1 8
## 591 2 80 0 4
## 829 2 80 1 9
## 672 3 80 2 6
## 653 3 80 0 20
## 832 4 80 0 0
## 371 3 80 1 4
## 380 2 80 0 7
## 97 4 80 1 23
## 49 4 80 0 0
## 469 4 80 0 7
## 260 3 80 1 7
## 650 2 80 0 10
## 174 4 80 2 10
## 775 3 80 1 24
## 554 1 80 2 13
## 723 3 80 2 10
## 183 4 80 0 18
## 347 4 80 0 5
## 74 1 80 1 6
## 641 2 80 1 1
## 631 2 80 1 19
## 118 1 80 1 10
## 271 2 80 2 10
## 551 2 80 1 22
## 763 1 80 0 19
## 357 3 80 0 5
## 252 4 80 2 12
## 619 1 80 1 9
## 566 3 80 0 6
## 459 4 80 1 5
## 61 1 80 1 16
## 179 4 80 0 9
## 272 3 80 1 11
## 76 4 80 0 12
## 87 4 80 0 20
## 224 4 80 1 1
## 739 2 80 0 5
## 164 3 80 0 10
## 13 2 80 0 17
## 526 1 80 1 30
## 836 4 80 1 9
## 693 2 80 0 14
## 545 3 80 0 6
## 334 4 80 0 9
## 95 2 80 0 21
## 657 4 80 2 16
## 269 3 80 3 9
## 53 4 80 1 10
## 367 3 80 2 15
## 420 3 80 0 10
## 54 2 80 0 9
## 383 1 80 3 15
## 571 3 80 1 14
## 274 1 80 2 11
## 773 4 80 0 20
## 168 3 80 1 10
## 786 3 80 0 9
## 546 2 80 0 10
## 191 4 80 2 11
## 572 1 80 0 1
## 673 4 80 0 5
## 67 2 80 0 17
## 580 2 80 2 6
## 869 3 80 1 7
## 277 4 80 1 2
## 828 4 80 0 4
## 34 2 80 1 10
## 504 3 80 1 10
## 586 3 80 1 15
## 562 2 80 0 23
## 618 3 80 1 9
## 615 1 80 1 10
## 694 3 80 0 3
## 127 2 80 0 10
## 31 4 80 3 9
## 793 4 80 1 10
## 830 3 80 1 32
## 308 4 80 3 6
## 863 2 80 0 9
## 68 3 80 0 5
## 707 2 80 1 14
## 807 2 80 0 6
## 351 2 80 1 18
## 582 2 80 0 16
## 11 4 80 0 21
## 149 4 80 0 6
## 666 4 80 0 10
## 857 3 80 0 8
## 589 1 80 0 8
## 349 4 80 0 6
## 742 3 80 1 10
## 648 3 80 3 3
## 199 4 80 1 24
## 624 3 80 0 9
## 565 3 80 2 7
## 310 3 80 1 4
## 754 3 80 3 14
## 604 1 80 1 10
## 795 1 80 2 7
## 519 1 80 1 10
## 295 4 80 0 21
## 558 4 80 1 6
## 364 4 80 1 22
## 408 3 80 1 7
## 567 4 80 0 9
## 235 4 80 2 25
## 122 4 80 0 2
## 628 2 80 1 12
## 157 3 80 1 15
## 819 4 80 0 6
## 821 4 80 2 6
## 120 4 80 0 14
## 346 1 80 0 21
## 539 2 80 0 6
## 439 3 80 0 3
## 256 3 80 0 7
## 498 3 80 3 4
## 679 2 80 3 18
## 165 3 80 2 12
## 352 2 80 1 10
## 198 2 80 1 22
## 353 4 80 1 16
## 758 2 80 1 20
## 852 1 80 0 10
## 330 3 80 1 21
## 152 4 80 0 10
## 404 3 80 2 9
## 384 3 80 1 3
## 454 2 80 0 10
## 418 4 80 2 4
## 372 3 80 0 10
## 106 2 80 1 8
## 285 4 80 1 7
## 23 2 80 1 10
## 399 1 80 1 13
## 465 4 80 1 6
## 1 3 80 1 8
## 583 3 80 1 15
## 241 4 80 1 10
## 51 4 80 0 8
## 255 3 80 1 16
## 470 3 80 2 5
## 341 4 80 1 6
## 45 4 80 1 1
## 121 1 80 1 10
## 507 2 80 0 1
## 710 4 80 1 31
## 348 1 80 1 6
## 381 2 80 1 21
## 201 4 80 0 22
## 581 3 80 3 7
## 517 3 80 3 1
## 419 2 80 1 14
## 772 3 80 3 24
## 64 3 80 0 0
## 223 3 80 1 7
## 146 1 80 1 4
## 25 1 80 0 6
## 446 3 80 1 17
## 448 3 80 2 10
## 150 2 80 1 6
## 782 1 80 0 20
## 853 4 80 0 10
## 766 1 80 0 10
## 537 3 80 0 10
## 2 1 80 0 21
## 161 3 80 2 10
## 75 3 80 0 10
## 318 2 80 0 4
## 414 3 80 0 4
## 681 1 80 0 5
## 186 4 80 1 5
## 135 2 80 1 9
## 732 1 80 0 12
## 785 4 80 1 4
## 770 4 80 1 15
## 801 1 80 2 10
## 779 3 80 0 7
## 547 3 80 1 29
## 278 1 80 0 9
## 555 4 80 1 5
## 263 3 80 1 8
## 438 4 80 0 8
## 206 4 80 0 8
## 302 4 80 1 9
## 192 4 80 2 9
## 590 3 80 1 4
## 834 3 80 0 1
## 768 3 80 1 20
## 733 1 80 0 1
## 237 1 80 0 24
## 321 3 80 0 5
## 125 1 80 0 10
## 784 1 80 1 10
## 286 2 80 1 15
## 682 1 80 1 8
## 143 2 80 1 13
## 275 4 80 0 36
## 436 1 80 1 8
## 156 2 80 1 4
## 532 1 80 0 15
## 842 1 80 0 10
## 747 4 80 3 4
## 866 3 80 0 11
## 113 4 80 0 18
## 535 4 80 0 7
## 596 1 80 1 9
## 162 1 80 0 10
## 825 2 80 2 7
## 728 4 80 1 5
## 332 3 80 0 14
## 702 1 80 0 6
## 870 4 80 0 10
## 636 2 80 1 2
## 777 1 80 1 6
## 128 4 80 2 10
## 610 4 80 0 3
## 818 4 80 0 14
## 451 1 80 1 10
## 712 3 80 1 32
## 490 4 80 0 10
## 222 4 80 2 5
## 647 4 80 0 0
## 85 4 80 0 18
## 806 4 80 1 15
## 516 4 80 1 13
## 637 4 80 1 23
## 159 3 80 1 18
## 289 3 80 1 10
## 635 1 80 1 10
## 464 4 80 0 9
## 654 2 80 1 15
## 299 1 80 0 4
## 788 1 80 0 11
## 761 3 80 0 3
## 664 1 80 1 3
## 331 1 80 0 23
## 204 4 80 0 10
## 57 4 80 0 7
## 29 2 80 0 15
## 734 4 80 0 6
## 55 4 80 0 11
## 862 4 80 0 23
## 279 4 80 0 6
## 472 2 80 0 1
## 190 2 80 0 5
## 132 3 80 0 8
## 409 3 80 0 31
## 557 3 80 0 19
## 340 4 80 1 1
## 437 4 80 0 5
## 844 4 80 1 10
## 238 4 80 1 6
## 329 3 80 1 20
## 229 1 80 1 4
## 423 2 80 0 12
## 805 3 80 1 17
## 656 3 80 1 5
## 435 2 80 1 11
## 492 2 80 0 25
## 187 3 80 2 7
## 575 4 80 2 6
## 671 1 80 0 16
## 560 2 80 1 8
## 401 3 80 0 11
## 176 3 80 0 2
## 715 2 80 0 10
## 621 2 80 0 1
## 823 4 80 2 8
## 815 4 80 1 3
## 133 4 80 3 7
## 425 3 80 1 5
## 38 2 80 3 16
## 692 4 80 1 10
## 292 3 80 1 11
## 690 4 80 3 13
## 833 3 80 3 6
## 503 3 80 2 19
## 21 3 80 1 13
## 112 2 80 1 26
## 124 1 80 0 8
## 422 1 80 2 15
## 91 3 80 1 1
## 259 4 80 3 9
## 453 4 80 0 10
## 35 2 80 0 8
## 17 3 80 1 8
## 677 1 80 1 10
## 415 4 80 1 3
## 813 4 80 1 5
## 32 4 80 3 14
## 320 4 80 0 1
## 496 3 80 1 29
## 225 4 80 2 10
## 226 3 80 0 12
## 385 3 80 3 18
## 700 1 80 1 25
## 411 3 80 0 6
## 822 4 80 1 10
## 393 4 80 1 4
## 799 2 80 0 1
## 324 1 80 3 19
## 267 4 80 2 16
## 698 4 80 0 16
## 264 2 80 1 3
## 110 4 80 0 4
## 494 4 80 0 11
## 42 1 80 0 9
## 245 3 80 1 10
## 489 4 80 0 23
## 300 4 80 0 5
## 652 2 80 1 12
## 688 4 80 0 7
## 94 3 80 0 10
## 377 3 80 0 18
## 467 4 80 0 7
## 394 4 80 0 22
## 445 3 80 1 24
## 524 3 80 1 31
## 680 2 80 0 13
## 253 4 80 0 1
## 177 4 80 1 24
## 500 3 80 1 10
## 506 1 80 1 12
## 858 1 80 1 10
## 549 3 80 1 19
## 674 1 80 1 10
## 443 3 80 0 5
## 14 1 80 1 1
## 333 3 80 3 6
## 663 2 80 1 13
## 530 1 80 3 7
## 3 3 80 0 10
## 523 3 80 1 25
## 369 4 80 1 4
## 129 1 80 0 20
## 633 3 80 0 19
## 48 2 80 2 9
## 325 3 80 1 21
## 845 1 80 0 6
## 7 1 80 0 7
## 847 4 80 0 1
## 716 3 80 1 1
## 28 1 80 0 6
## 230 2 80 1 30
## 416 3 80 1 15
## 373 1 80 1 24
## 809 2 80 1 10
## 729 2 80 0 1
## 812 2 80 1 7
## 528 4 80 0 6
## 37 3 80 0 15
## 705 4 80 0 7
## 354 3 80 0 8
## 563 2 80 1 4
## 559 4 80 1 10
## 525 1 80 1 7
## 868 3 80 1 11
## 27 4 80 1 10
## 755 1 80 1 16
## 594 4 80 1 10
## 520 4 80 0 5
## 287 1 80 0 1
## 620 3 80 2 1
## 753 3 80 1 32
## 65 1 80 1 1
## 154 1 80 0 11
## 662 3 80 1 14
## 247 3 80 0 11
## 850 1 80 1 8
## 33 2 80 1 28
## 550 1 80 0 6
## 797 2 80 0 21
## 509 3 80 1 13
## 24 4 80 0 10
## 185 1 80 1 8
## 513 1 80 1 10
## 553 4 80 0 10
## 455 1 80 1 21
## 477 4 80 2 9
## 360 3 80 1 16
## 846 4 80 1 10
## 217 3 80 1 18
## 765 3 80 1 4
## 714 1 80 1 16
## 593 4 80 1 5
## 699 3 80 0 0
## 762 3 80 1 26
## 824 3 80 1 2
## 407 1 80 2 13
## 391 2 80 1 6
## 153 3 80 1 13
## 433 1 80 0 20
## 82 2 80 1 17
## 831 3 80 1 9
## 232 1 80 0 2
## 796 1 80 0 10
## 685 3 80 1 5
## 317 4 80 1 6
## 242 3 80 0 22
## 290 2 80 1 3
## 119 1 80 1 13
## 808 1 80 2 2
## 6 3 80 2 9
## 311 2 80 1 9
## 392 3 80 1 5
## 481 1 80 1 6
## 114 1 80 1 16
## 864 4 80 2 28
## 683 2 80 2 10
## 368 4 80 0 9
## 622 3 80 0 20
## 314 3 80 0 10
## 306 4 80 0 6
## 412 2 80 0 10
## 219 3 80 0 5
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 284 3 3 7 7
## 848 5 2 18 10
## 101 3 4 5 0
## 623 2 3 4 2
## 645 2 1 1 0
## 400 2 2 2 2
## 98 2 4 1 0
## 103 3 3 3 2
## 726 3 3 4 2
## 602 3 2 6 5
## 326 2 3 1 1
## 79 2 3 19 7
## 270 1 2 6 2
## 382 3 3 8 7
## 184 3 3 2 0
## 574 5 3 10 8
## 4 3 3 14 10
## 661 2 3 10 8
## 552 6 3 5 1
## 212 3 4 1 0
## 195 2 3 4 3
## 511 1 3 10 7
## 479 2 1 5 4
## 605 3 3 2 2
## 634 2 3 1 0
## 578 5 3 5 2
## 510 0 3 22 15
## 687 1 3 8 7
## 424 5 4 0 0
## 379 2 2 5 4
## 816 5 3 14 10
## 108 0 3 10 9
## 131 3 2 10 0
## 343 2 3 3 2
## 41 0 2 16 9
## 627 3 3 5 4
## 740 3 3 3 2
## 298 3 3 9 7
## 811 4 3 12 9
## 258 3 3 3 2
## 629 4 3 3 2
## 859 2 3 5 0
## 182 3 3 7 7
## 305 4 3 1 0
## 358 2 4 2 2
## 696 3 2 3 2
## 307 5 3 1 0
## 827 2 3 1 0
## 221 5 3 6 5
## 736 5 2 8 7
## 561 3 2 8 7
## 313 2 1 7 2
## 136 2 3 8 7
## 794 2 3 17 14
## 145 0 3 12 11
## 123 2 3 5 4
## 776 3 3 2 2
## 234 4 3 6 2
## 608 3 3 8 0
## 495 4 3 10 9
## 534 6 3 5 1
## 803 3 3 21 9
## 297 2 2 10 9
## 208 2 3 1 0
## 838 3 3 10 7
## 569 6 3 5 4
## 522 3 3 1 1
## 248 2 3 2 2
## 365 5 3 19 9
## 665 2 3 9 5
## 643 3 4 9 7
## 595 2 3 18 15
## 434 2 3 1 0
## 757 2 1 10 6
## 760 2 3 24 10
## 218 3 3 22 17
## 727 3 3 5 4
## 508 5 3 4 2
## 276 2 3 5 2
## 169 3 3 10 9
## 71 3 3 5 3
## 573 2 3 7 7
## 860 2 3 10 7
## 485 3 2 40 10
## 667 5 3 11 10
## 460 2 2 10 4
## 60 4 3 1 0
## 449 2 3 10 3
## 548 6 3 1 0
## 19 3 3 5 0
## 649 2 3 15 13
## 638 2 2 8 7
## 670 2 3 10 9
## 319 2 4 5 4
## 116 2 3 10 0
## 840 3 3 22 10
## 102 5 3 13 7
## 214 4 3 4 3
## 390 3 3 3 2
## 597 2 3 13 10
## 771 6 2 7 7
## 160 6 3 1 0
## 77 3 3 10 9
## 529 3 3 6 4
## 126 2 3 10 8
## 262 2 1 9 7
## 442 2 3 5 3
## 642 3 3 7 4
## 181 2 4 7 7
## 163 3 3 5 0
## 474 2 2 6 5
## 228 2 2 4 2
## 774 0 2 1 0
## 646 2 2 12 11
## 265 2 3 5 3
## 427 2 2 1 0
## 781 5 2 4 3
## 249 4 2 6 2
## 810 3 2 5 2
## 40 4 3 2 2
## 541 4 2 10 7
## 497 3 2 2 2
## 697 3 3 2 2
## 865 4 4 3 1
## 450 2 3 10 7
## 600 2 2 2 0
## 778 3 3 1 0
## 363 3 3 32 14
## 478 5 3 0 0
## 403 2 3 2 2
## 375 3 3 2 2
## 335 3 3 3 2
## 598 3 3 1 0
## 142 3 3 2 2
## 444 2 3 2 2
## 741 3 3 2 2
## 659 3 3 5 4
## 790 3 2 2 2
## 457 2 3 10 0
## 188 2 3 3 2
## 432 0 3 4 2
## 488 1 2 5 2
## 538 5 3 3 2
## 752 2 4 3 2
## 215 0 3 4 2
## 540 3 4 6 4
## 613 3 4 9 7
## 730 2 3 2 1
## 296 3 3 2 2
## 328 3 2 4 3
## 147 5 3 8 7
## 701 3 3 1 0
## 708 3 3 3 2
## 84 2 3 7 7
## 83 1 3 8 3
## 250 2 3 18 16
## 767 3 3 10 6
## 281 3 3 10 9
## 675 0 3 3 2
## 843 3 3 9 8
## 431 3 3 5 4
## 30 3 3 4 3
## 10 3 2 8 2
## 817 4 2 0 0
## 441 2 3 8 7
## 820 0 3 2 0
## 345 3 3 4 3
## 592 2 4 15 9
## 585 5 4 4 2
## 660 3 3 6 5
## 12 2 3 3 2
## 293 2 2 7 7
## 303 1 3 4 2
## 738 2 3 6 1
## 678 2 4 10 9
## 338 1 2 6 2
## 350 1 2 10 7
## 720 3 2 9 7
## 658 2 3 14 9
## 107 1 3 10 7
## 543 2 2 1 1
## 518 6 2 5 3
## 854 2 3 8 3
## 43 2 2 2 2
## 189 4 2 10 4
## 171 2 2 8 7
## 39 4 3 7 3
## 216 3 4 3 1
## 291 2 3 5 3
## 58 3 3 10 9
## 395 2 3 2 1
## 856 3 3 1 0
## 456 3 2 21 8
## 22 3 3 2 2
## 170 1 3 7 5
## 588 4 3 5 3
## 63 1 3 2 2
## 676 2 3 11 4
## 719 2 4 11 9
## 417 2 2 11 7
## 789 2 3 1 0
## 70 3 3 17 12
## 59 2 3 5 2
## 800 3 3 5 4
## 484 3 3 2 2
## 203 3 3 14 13
## 227 3 3 1 0
## 243 3 3 5 4
## 413 1 3 1 0
## 577 3 2 1 0
## 542 1 3 10 8
## 476 4 3 9 6
## 744 3 1 3 2
## 804 2 3 8 7
## 405 3 2 6 5
## 397 2 1 3 2
## 607 2 2 4 3
## 501 0 2 10 9
## 429 2 1 3 2
## 205 5 3 4 3
## 475 5 2 4 2
## 104 3 4 3 2
## 210 2 3 9 7
## 471 3 2 5 3
## 66 5 2 6 2
## 88 2 1 4 2
## 826 0 3 2 2
## 601 2 3 1 0
## 309 3 3 8 7
## 737 2 2 20 9
## 294 3 1 13 8
## 421 2 3 5 2
## 769 3 2 6 3
## 430 3 3 11 9
## 512 2 3 6 4
## 361 5 3 33 18
## 814 2 3 5 2
## 480 2 3 16 11
## 15 3 4 7 6
## 486 0 1 6 4
## 254 4 3 1 0
## 491 4 3 3 2
## 713 4 2 1 0
## 178 2 3 3 2
## 428 4 4 3 2
## 691 2 3 6 3
## 194 2 3 9 7
## 599 0 3 6 2
## 802 2 4 6 5
## 378 4 3 0 0
## 639 3 3 6 5
## 155 3 3 1 0
## 166 3 2 2 2
## 207 2 2 4 3
## 468 3 3 1 0
## 105 2 2 9 8
## 482 2 3 6 4
## 587 3 3 13 11
## 280 2 3 3 2
## 440 4 2 9 7
## 389 2 2 13 7
## 499 3 2 7 7
## 251 2 2 1 1
## 231 3 3 10 7
## 564 2 4 11 7
## 640 3 3 4 3
## 463 3 3 2 2
## 100 2 2 5 4
## 731 2 4 1 0
## 141 0 3 19 16
## 841 2 3 0 0
## 745 3 2 5 3
## 36 3 1 5 1
## 792 3 3 3 2
## 362 3 3 1 0
## 86 4 3 5 3
## 138 2 3 10 7
## 344 2 3 1 0
## 211 3 3 20 7
## 461 4 3 2 2
## 591 2 2 4 2
## 829 4 2 6 1
## 672 5 3 6 2
## 653 3 2 18 7
## 832 2 2 0 0
## 371 1 3 4 3
## 380 2 3 5 2
## 97 2 4 13 12
## 49 3 3 0 0
## 469 6 3 2 2
## 260 2 2 2 2
## 650 3 1 4 2
## 174 3 3 9 8
## 775 3 1 20 8
## 554 2 2 0 0
## 723 2 3 4 3
## 183 2 3 3 2
## 347 3 2 3 2
## 74 4 3 1 0
## 641 2 3 1 0
## 631 2 4 5 2
## 118 2 3 4 3
## 271 3 4 8 7
## 551 3 3 22 7
## 763 4 3 19 2
## 357 3 2 5 4
## 252 3 3 7 7
## 619 5 3 7 7
## 566 2 3 4 3
## 459 3 4 3 2
## 61 6 3 16 7
## 179 2 3 9 8
## 272 2 3 11 8
## 76 2 3 12 8
## 87 2 3 7 7
## 224 2 1 1 0
## 739 3 3 5 2
## 164 1 3 3 2
## 13 3 4 8 5
## 526 2 3 15 7
## 836 6 3 9 5
## 693 6 3 1 0
## 545 3 3 6 5
## 334 3 3 9 7
## 95 5 3 5 4
## 657 5 1 10 9
## 269 2 3 9 7
## 53 5 3 10 5
## 367 2 4 11 8
## 420 2 3 10 2
## 54 5 2 9 7
## 383 3 3 2 2
## 571 3 3 5 4
## 274 3 2 3 2
## 773 1 3 19 6
## 168 2 3 6 1
## 786 3 4 3 2
## 546 3 3 10 9
## 191 2 3 10 8
## 572 3 2 1 0
## 673 5 3 4 2
## 67 3 3 17 11
## 580 2 3 6 0
## 869 2 2 3 2
## 277 1 3 2 1
## 828 3 3 3 2
## 34 2 3 8 0
## 504 1 3 10 9
## 586 2 4 7 2
## 562 3 3 2 2
## 618 2 2 4 3
## 615 2 4 8 4
## 694 4 3 1 0
## 127 5 3 10 2
## 31 2 4 8 7
## 793 6 4 5 4
## 830 2 3 32 5
## 308 2 3 0 0
## 863 3 2 5 2
## 68 6 4 5 2
## 707 2 2 7 1
## 807 3 2 3 2
## 351 3 4 13 7
## 582 2 3 1 0
## 11 2 3 16 12
## 149 2 3 4 3
## 666 5 4 3 2
## 857 5 3 5 2
## 589 6 3 3 2
## 349 3 3 0 0
## 742 3 2 5 4
## 648 3 4 3 2
## 199 4 3 22 6
## 624 2 3 9 0
## 565 2 3 2 2
## 310 4 3 3 2
## 754 2 3 8 7
## 604 5 3 10 8
## 795 0 3 7 7
## 519 6 3 9 2
## 295 2 3 21 7
## 558 3 3 6 5
## 364 5 3 21 7
## 408 4 3 7 7
## 567 5 3 6 2
## 235 3 4 25 12
## 122 6 3 2 2
## 628 3 3 11 10
## 157 4 3 11 8
## 819 3 3 5 3
## 821 3 3 3 2
## 120 2 4 14 11
## 346 3 3 20 8
## 539 4 3 5 3
## 439 2 3 3 2
## 256 2 2 3 2
## 498 3 2 4 2
## 679 3 3 4 2
## 165 3 2 11 9
## 352 3 2 8 7
## 198 3 3 9 8
## 353 3 1 1 1
## 758 0 2 3 2
## 852 5 3 10 0
## 330 5 3 20 7
## 152 2 2 10 7
## 404 6 3 3 2
## 384 3 3 0 0
## 454 2 2 10 4
## 418 2 3 1 0
## 372 3 3 9 8
## 106 2 3 8 7
## 285 2 4 7 6
## 23 3 3 8 7
## 399 2 2 13 12
## 465 4 3 1 0
## 1 3 2 5 2
## 583 2 3 1 0
## 241 5 2 10 9
## 51 3 2 0 0
## 255 5 3 9 8
## 470 3 3 4 2
## 341 3 4 1 1
## 45 2 3 1 0
## 121 2 3 8 7
## 507 2 3 1 0
## 710 3 3 31 6
## 348 6 1 6 5
## 381 2 3 3 2
## 201 2 2 1 0
## 581 6 2 3 2
## 517 2 3 1 0
## 419 2 2 14 8
## 772 2 3 24 13
## 64 4 1 0 0
## 223 2 3 6 5
## 146 3 2 4 3
## 25 3 3 5 0
## 446 2 2 6 5
## 448 2 2 0 0
## 150 5 2 5 3
## 782 4 3 20 7
## 853 3 3 9 5
## 766 3 3 10 7
## 537 2 3 1 0
## 2 2 4 20 7
## 161 1 2 10 8
## 75 2 2 8 7
## 318 2 2 4 3
## 414 2 4 0 0
## 681 1 4 5 2
## 186 3 2 5 2
## 135 3 4 9 7
## 732 3 2 1 0
## 785 3 1 1 0
## 770 2 3 15 10
## 801 3 3 5 3
## 779 5 3 7 7
## 547 3 2 26 9
## 278 5 3 8 7
## 555 5 1 3 2
## 263 2 1 5 4
## 438 6 2 7 7
## 206 0 3 8 7
## 302 3 2 9 8
## 192 3 3 3 2
## 590 3 3 2 2
## 834 4 3 1 1
## 768 2 3 1 0
## 733 2 3 1 0
## 237 2 2 22 6
## 321 3 3 3 0
## 125 3 2 10 7
## 784 3 2 10 7
## 286 3 3 15 14
## 682 1 3 3 2
## 143 3 3 8 7
## 275 3 3 24 15
## 436 3 3 8 7
## 156 2 4 2 2
## 532 5 3 1 0
## 842 3 2 10 9
## 747 3 3 2 2
## 866 2 3 10 9
## 113 4 3 8 6
## 535 4 2 7 7
## 596 5 3 9 8
## 162 2 2 10 7
## 825 2 3 3 2
## 728 4 4 5 3
## 332 2 2 14 8
## 702 3 2 6 5
## 870 5 3 6 2
## 636 2 4 2 2
## 777 3 4 5 3
## 128 2 2 9 8
## 610 2 2 2 2
## 818 3 4 13 9
## 451 6 3 9 7
## 712 3 2 1 0
## 490 2 3 5 1
## 222 4 3 1 1
## 647 0 3 0 0
## 85 2 3 16 14
## 806 1 3 12 8
## 516 3 3 4 1
## 637 3 3 22 6
## 159 1 3 8 7
## 289 6 3 9 7
## 635 1 2 1 0
## 464 3 2 5 4
## 654 3 1 0 0
## 299 0 3 3 2
## 788 3 2 8 2
## 761 5 3 0 0
## 664 4 2 3 2
## 331 2 3 8 7
## 204 4 3 7 7
## 57 2 3 2 2
## 29 2 2 2 2
## 734 2 3 4 2
## 55 2 3 11 7
## 862 3 4 21 7
## 279 1 3 6 4
## 472 3 2 1 0
## 190 3 4 5 4
## 132 1 4 5 1
## 409 3 3 25 8
## 557 3 3 1 0
## 340 6 3 1 0
## 437 3 2 2 2
## 844 0 2 9 7
## 238 2 2 6 4
## 329 3 3 20 11
## 229 2 2 3 2
## 423 3 2 7 7
## 805 3 4 3 2
## 656 3 3 5 3
## 435 2 2 1 0
## 492 2 3 3 2
## 187 6 3 7 7
## 575 5 4 5 3
## 671 3 4 5 2
## 560 3 3 8 7
## 401 2 3 5 4
## 176 6 4 2 2
## 715 1 3 10 7
## 621 5 3 1 0
## 823 2 3 8 7
## 815 3 3 2 2
## 133 4 4 6 5
## 425 0 3 5 3
## 38 3 3 2 2
## 692 2 2 10 7
## 292 3 3 9 8
## 690 3 3 11 10
## 833 2 3 6 5
## 503 3 3 9 7
## 21 2 3 3 2
## 112 2 2 9 8
## 124 2 3 7 7
## 422 2 3 13 11
## 91 3 3 1 0
## 259 3 3 5 3
## 453 2 3 10 7
## 35 1 3 1 0
## 17 2 4 8 7
## 677 2 3 9 7
## 415 3 2 2 2
## 813 2 4 5 2
## 32 6 3 14 11
## 320 2 3 1 0
## 496 2 2 20 6
## 225 0 4 5 2
## 226 6 2 12 8
## 385 2 4 10 0
## 700 2 3 4 2
## 411 3 3 5 4
## 822 2 3 3 2
## 393 3 4 4 2
## 799 2 4 1 1
## 324 2 3 16 13
## 267 3 2 14 8
## 698 3 4 13 11
## 264 5 3 3 1
## 110 4 3 4 2
## 494 3 1 11 8
## 42 3 3 8 7
## 245 2 2 10 7
## 489 2 3 1 0
## 300 4 2 3 2
## 652 3 3 11 7
## 688 3 3 3 2
## 94 4 3 5 2
## 377 1 3 17 13
## 467 3 2 3 2
## 394 5 4 18 13
## 445 3 3 2 1
## 524 5 3 31 9
## 680 2 4 7 7
## 253 3 3 1 0
## 177 2 3 5 2
## 500 1 3 3 2
## 506 2 3 7 1
## 858 2 3 10 9
## 549 2 3 10 8
## 674 3 2 10 2
## 443 5 1 0 0
## 14 3 3 1 0
## 333 2 2 5 3
## 663 5 2 10 6
## 530 2 3 5 4
## 3 2 3 2 2
## 523 2 3 1 0
## 369 2 3 2 2
## 129 3 1 20 7
## 633 1 3 1 0
## 48 2 3 8 7
## 325 3 3 21 6
## 845 3 3 2 2
## 7 5 2 4 2
## 847 3 3 1 0
## 716 2 3 1 0
## 28 3 3 5 2
## 230 3 3 15 11
## 416 6 3 7 7
## 373 2 3 24 9
## 809 2 2 10 0
## 729 3 1 1 0
## 812 2 2 3 2
## 528 2 2 6 5
## 37 3 3 13 12
## 705 2 2 2 2
## 354 2 3 2 2
## 563 2 3 2 2
## 559 3 2 9 8
## 525 6 3 1 0
## 868 4 2 5 4
## 27 3 3 9 8
## 755 5 1 4 3
## 594 3 2 9 6
## 520 3 3 4 2
## 287 2 3 1 0
## 620 3 3 1 0
## 753 3 3 30 8
## 65 3 1 1 0
## 154 3 1 3 2
## 662 3 3 7 3
## 247 2 2 11 9
## 850 3 3 7 7
## 33 2 2 7 7
## 550 3 3 6 3
## 797 2 3 20 7
## 509 2 3 9 8
## 24 3 3 5 2
## 185 3 3 8 3
## 513 3 2 5 2
## 553 2 2 10 1
## 455 3 1 3 2
## 477 2 3 1 0
## 360 4 3 15 13
## 846 2 3 10 0
## 217 4 3 1 0
## 765 1 1 3 2
## 714 2 3 13 9
## 593 3 4 3 2
## 699 2 4 0 0
## 762 3 2 26 14
## 824 5 2 2 2
## 407 3 3 12 7
## 391 1 3 3 2
## 153 3 2 13 8
## 433 2 3 4 3
## 82 3 3 7 7
## 831 2 3 9 8
## 232 3 3 2 2
## 796 3 3 10 8
## 685 2 1 5 2
## 317 3 3 4 3
## 242 4 3 0 0
## 290 1 2 3 2
## 119 2 3 9 8
## 808 6 3 2 2
## 6 4 2 9 7
## 311 3 2 2 2
## 392 2 2 2 2
## 481 5 2 5 3
## 114 2 4 1 0
## 864 2 2 22 2
## 683 2 2 3 2
## 368 5 4 8 4
## 622 3 4 19 10
## 314 2 3 1 0
## 306 2 2 5 2
## 412 3 3 10 8
## 219 4 2 5 4
## YearsSinceLastPromotion YearsWithCurrManager
## 284 1 7
## 848 3 7
## 101 1 4
## 623 1 3
## 645 0 0
## 400 2 2
## 98 0 0
## 103 1 2
## 726 1 2
## 602 1 2
## 326 0 0
## 79 12 8
## 270 0 2
## 382 5 7
## 184 2 2
## 574 4 8
## 4 5 7
## 661 4 7
## 552 0 4
## 212 0 0
## 195 1 1
## 511 1 9
## 479 0 4
## 605 2 2
## 634 0 0
## 578 0 3
## 510 15 8
## 687 7 7
## 424 0 0
## 379 1 3
## 816 4 10
## 108 1 4
## 131 0 9
## 343 1 2
## 41 14 14
## 627 0 3
## 740 0 2
## 298 0 6
## 811 6 10
## 258 0 2
## 629 1 2
## 859 1 4
## 182 1 7
## 305 0 0
## 358 1 1
## 696 1 2
## 307 0 0
## 827 0 0
## 221 1 4
## 736 0 7
## 561 7 2
## 313 7 7
## 136 3 7
## 794 12 8
## 145 11 8
## 123 0 2
## 776 0 2
## 234 0 5
## 608 7 7
## 495 1 7
## 534 1 2
## 803 11 10
## 297 1 9
## 208 0 0
## 838 5 7
## 569 0 3
## 522 0 0
## 248 2 2
## 365 9 11
## 665 8 7
## 643 6 2
## 595 14 12
## 434 0 0
## 757 0 7
## 760 1 11
## 218 4 7
## 727 0 0
## 508 3 2
## 276 4 2
## 169 6 9
## 71 1 0
## 573 0 7
## 860 0 5
## 485 15 6
## 667 4 1
## 460 1 8
## 60 1 0
## 449 9 9
## 548 0 0
## 19 1 2
## 649 2 8
## 638 1 1
## 670 0 9
## 319 1 4
## 116 0 9
## 840 12 9
## 102 9 9
## 214 0 2
## 390 1 2
## 597 4 8
## 771 0 1
## 160 0 0
## 77 8 6
## 529 0 4
## 126 0 9
## 262 0 7
## 442 1 2
## 642 7 7
## 181 6 4
## 163 1 2
## 474 0 3
## 228 2 2
## 774 0 0
## 646 2 11
## 265 1 2
## 427 0 0
## 781 0 2
## 249 3 3
## 810 1 1
## 40 2 2
## 541 8 9
## 497 0 2
## 697 2 2
## 865 1 2
## 450 4 5
## 600 2 0
## 778 0 0
## 363 6 9
## 478 0 0
## 403 2 0
## 375 0 2
## 335 2 2
## 598 0 0
## 142 2 2
## 444 2 2
## 741 2 2
## 659 1 2
## 790 2 1
## 457 0 2
## 188 1 2
## 432 0 0
## 488 4 3
## 538 1 0
## 752 2 2
## 215 1 1
## 540 1 5
## 613 1 7
## 730 2 1
## 296 2 2
## 328 1 2
## 147 1 6
## 701 0 0
## 708 0 2
## 84 6 7
## 83 7 7
## 250 11 8
## 767 0 8
## 281 8 8
## 675 0 2
## 843 0 0
## 431 0 4
## 30 1 2
## 10 7 7
## 817 0 0
## 441 0 7
## 820 2 2
## 345 0 3
## 592 10 10
## 585 1 3
## 660 0 3
## 12 2 2
## 293 0 3
## 303 1 2
## 738 0 5
## 678 9 4
## 338 0 4
## 350 1 1
## 720 1 7
## 658 1 12
## 107 0 9
## 543 0 1
## 518 0 4
## 854 0 7
## 43 2 2
## 189 1 6
## 171 7 7
## 39 7 7
## 216 1 2
## 291 0 2
## 58 1 5
## 395 2 2
## 856 0 0
## 456 12 8
## 22 2 2
## 170 1 7
## 588 0 3
## 63 1 2
## 676 0 8
## 719 6 7
## 417 6 7
## 789 0 0
## 70 5 7
## 59 0 2
## 800 0 4
## 484 2 2
## 203 6 8
## 227 0 0
## 243 0 4
## 413 0 0
## 577 0 0
## 542 7 6
## 476 0 8
## 744 1 2
## 804 4 7
## 405 1 1
## 397 0 1
## 607 1 3
## 501 5 9
## 429 1 2
## 205 1 2
## 475 2 2
## 104 0 2
## 210 0 7
## 471 1 2
## 66 0 5
## 88 0 2
## 826 2 2
## 601 0 0
## 309 6 7
## 737 9 6
## 294 5 12
## 421 1 3
## 769 1 4
## 430 5 9
## 512 0 5
## 361 11 9
## 814 1 0
## 480 6 8
## 15 5 7
## 486 0 5
## 254 0 0
## 491 1 2
## 713 0 0
## 178 1 2
## 428 1 0
## 691 1 2
## 194 6 7
## 599 0 3
## 802 0 5
## 378 0 0
## 639 1 4
## 155 1 0
## 166 0 2
## 207 1 3
## 468 0 0
## 105 8 8
## 482 0 2
## 587 1 9
## 280 1 2
## 440 1 8
## 389 6 7
## 499 7 7
## 251 0 0
## 231 3 9
## 564 4 8
## 640 0 2
## 463 2 1
## 100 0 2
## 731 0 0
## 141 15 9
## 841 0 0
## 745 0 4
## 36 0 3
## 792 1 2
## 362 0 0
## 86 4 3
## 138 0 8
## 344 0 0
## 211 0 10
## 461 2 0
## 591 1 3
## 829 0 5
## 672 0 4
## 653 2 13
## 832 0 0
## 371 0 3
## 380 0 1
## 97 5 1
## 49 0 0
## 469 2 2
## 260 0 2
## 650 0 3
## 174 0 8
## 775 13 9
## 554 0 0
## 723 1 3
## 183 1 2
## 347 0 2
## 74 0 0
## 641 0 0
## 631 0 4
## 118 0 3
## 271 5 7
## 551 2 10
## 763 11 9
## 357 4 3
## 252 0 7
## 619 1 7
## 566 1 2
## 459 1 0
## 61 3 7
## 179 3 7
## 272 7 9
## 76 3 7
## 87 1 7
## 224 0 0
## 739 1 0
## 164 0 2
## 13 1 6
## 526 6 12
## 836 7 7
## 693 0 0
## 545 1 3
## 334 0 7
## 95 4 4
## 657 4 7
## 269 2 8
## 53 7 7
## 367 5 10
## 420 0 7
## 54 0 8
## 383 2 2
## 571 1 4
## 274 0 2
## 773 11 8
## 168 0 5
## 786 1 0
## 546 8 7
## 191 1 9
## 572 0 0
## 673 1 3
## 67 11 8
## 580 1 0
## 869 0 2
## 277 1 2
## 828 0 2
## 34 1 7
## 504 8 8
## 586 3 7
## 562 2 2
## 618 1 3
## 615 7 7
## 694 0 0
## 127 6 7
## 31 0 7
## 793 0 2
## 830 10 7
## 308 0 0
## 863 0 4
## 68 1 4
## 707 1 7
## 807 1 2
## 351 5 7
## 582 0 0
## 11 6 14
## 149 1 2
## 666 0 2
## 857 0 4
## 589 0 2
## 349 0 0
## 742 0 1
## 648 0 2
## 199 5 17
## 624 1 7
## 565 0 2
## 310 1 2
## 754 0 7
## 604 0 8
## 795 1 7
## 519 6 7
## 295 7 7
## 558 1 3
## 364 3 9
## 408 1 7
## 567 0 4
## 235 4 12
## 122 1 2
## 628 2 9
## 157 5 10
## 819 1 2
## 821 0 2
## 120 4 11
## 346 11 10
## 539 1 4
## 439 0 2
## 256 1 2
## 498 1 2
## 679 0 2
## 165 6 9
## 352 7 7
## 198 2 3
## 353 0 0
## 758 1 2
## 852 1 8
## 330 0 9
## 152 0 8
## 404 0 2
## 384 0 0
## 454 0 9
## 418 0 0
## 372 7 8
## 106 0 7
## 285 5 0
## 23 4 7
## 399 1 9
## 465 0 0
## 1 0 3
## 583 1 0
## 241 5 8
## 51 0 0
## 255 4 8
## 470 1 3
## 341 0 0
## 45 0 1
## 121 0 5
## 507 0 0
## 710 14 7
## 348 1 4
## 381 1 1
## 201 0 0
## 581 1 2
## 517 0 0
## 419 3 11
## 772 15 7
## 64 0 0
## 223 1 2
## 146 0 2
## 25 1 4
## 446 1 2
## 448 0 0
## 150 0 0
## 782 11 10
## 853 0 8
## 766 1 4
## 537 0 0
## 2 4 9
## 161 3 0
## 75 7 7
## 318 1 2
## 414 0 0
## 681 0 3
## 186 0 4
## 135 0 0
## 732 0 0
## 785 0 0
## 770 4 12
## 801 1 3
## 779 5 7
## 547 1 7
## 278 1 7
## 555 0 2
## 263 0 4
## 438 7 6
## 206 7 1
## 302 1 8
## 192 2 2
## 590 2 2
## 834 0 0
## 768 0 1
## 733 0 0
## 237 4 14
## 321 0 2
## 125 0 9
## 784 0 1
## 286 5 7
## 682 1 2
## 143 0 7
## 275 2 15
## 436 0 7
## 156 2 2
## 532 0 0
## 842 0 9
## 747 2 0
## 866 0 8
## 113 4 0
## 535 0 7
## 596 0 8
## 162 1 9
## 825 1 1
## 728 2 0
## 332 9 8
## 702 1 3
## 870 1 2
## 636 2 2
## 777 1 3
## 128 3 8
## 610 2 2
## 818 3 7
## 451 8 1
## 712 0 0
## 490 4 3
## 222 0 0
## 647 0 0
## 85 5 12
## 806 5 7
## 516 1 2
## 637 13 7
## 159 0 1
## 289 2 8
## 635 0 0
## 464 1 4
## 654 0 0
## 299 1 2
## 788 7 7
## 761 0 0
## 664 1 2
## 331 0 0
## 204 3 7
## 57 2 2
## 29 2 2
## 734 1 2
## 55 1 8
## 862 15 17
## 279 0 3
## 472 1 0
## 190 0 4
## 132 0 4
## 409 3 7
## 557 0 0
## 340 0 0
## 437 2 1
## 844 1 6
## 238 0 5
## 329 0 7
## 229 0 2
## 423 7 7
## 805 1 2
## 656 3 3
## 435 0 0
## 492 1 2
## 187 0 7
## 575 1 4
## 671 0 2
## 560 7 7
## 401 0 2
## 176 2 2
## 715 0 9
## 621 1 0
## 823 1 7
## 815 1 2
## 133 0 4
## 425 0 4
## 38 2 2
## 692 9 9
## 292 0 8
## 690 3 8
## 833 3 3
## 503 7 7
## 21 0 2
## 112 7 8
## 124 0 5
## 422 10 7
## 91 0 0
## 259 1 0
## 453 7 7
## 35 0 0
## 17 6 3
## 677 0 5
## 415 1 0
## 813 0 3
## 32 2 13
## 320 0 1
## 496 4 17
## 225 0 3
## 226 1 7
## 385 2 7
## 700 0 3
## 411 0 3
## 822 0 2
## 393 2 3
## 799 0 0
## 324 1 7
## 267 6 9
## 698 3 7
## 264 0 2
## 110 1 2
## 494 3 3
## 42 3 1
## 245 1 2
## 489 0 0
## 300 2 2
## 652 1 9
## 688 1 1
## 94 0 4
## 377 15 14
## 467 1 2
## 394 13 11
## 445 2 2
## 524 0 9
## 680 5 2
## 253 0 0
## 177 1 4
## 500 1 2
## 506 2 5
## 858 8 9
## 549 0 1
## 674 7 8
## 443 0 0
## 14 0 0
## 333 2 3
## 663 0 3
## 530 4 3
## 3 2 2
## 523 0 0
## 369 2 2
## 129 1 8
## 633 0 0
## 48 2 7
## 325 11 8
## 845 2 2
## 7 0 3
## 847 0 0
## 716 0 0
## 28 0 3
## 230 2 12
## 416 1 7
## 373 9 11
## 809 0 8
## 729 0 0
## 812 0 2
## 528 0 1
## 37 6 0
## 705 2 2
## 354 2 2
## 563 2 2
## 559 7 8
## 525 0 0
## 868 1 2
## 27 7 7
## 755 0 3
## 594 1 4
## 520 1 0
## 287 0 0
## 620 0 0
## 753 12 13
## 65 0 0
## 154 1 2
## 662 5 7
## 247 4 10
## 850 7 7
## 33 7 7
## 550 0 4
## 797 4 10
## 509 5 8
## 24 0 3
## 185 0 7
## 513 1 3
## 553 0 8
## 455 0 2
## 477 0 0
## 360 10 11
## 846 1 8
## 217 0 0
## 765 0 2
## 714 1 12
## 593 1 2
## 699 0 0
## 762 3 0
## 824 2 2
## 407 5 7
## 391 1 2
## 153 4 8
## 433 1 3
## 82 0 7
## 831 0 8
## 232 2 2
## 796 9 7
## 685 0 2
## 317 1 2
## 242 0 0
## 290 0 2
## 119 1 8
## 808 2 2
## 6 1 7
## 311 2 1
## 392 0 0
## 481 0 2
## 114 0 0
## 864 11 13
## 683 0 2
## 368 7 1
## 622 2 7
## 314 0 0
## 306 3 4
## 412 8 7
## 219 0 4SalaryTest = Attritiondata[-TrainObs,]
SalaryTest## ID Age Attrition BusinessTravel DailyRate Department
## 5 5 24 No Travel_Frequently 567 Research & Development
## 8 8 37 No Travel_Rarely 309 Sales
## 9 9 34 No Travel_Rarely 1333 Sales
## 16 16 31 No Non-Travel 1188 Sales
## 18 18 46 No Non-Travel 1144 Research & Development
## 20 20 44 No Travel_Rarely 170 Research & Development
## 26 26 44 No Travel_Rarely 1099 Sales
## 44 44 42 No Travel_Rarely 1059 Research & Development
## 46 46 42 No Travel_Frequently 748 Research & Development
## 47 47 32 Yes Travel_Rarely 964 Sales
## 50 50 27 No Non-Travel 691 Research & Development
## 52 52 58 No Travel_Rarely 1055 Research & Development
## 56 56 41 No Travel_Rarely 548 Research & Development
## 62 62 36 No Travel_Rarely 325 Research & Development
## 69 69 35 No Travel_Frequently 664 Research & Development
## 72 72 28 No Travel_Rarely 866 Sales
## 73 73 35 Yes Travel_Rarely 556 Research & Development
## 78 78 46 No Travel_Rarely 563 Sales
## 80 80 31 No Travel_Rarely 1276 Research & Development
## 81 81 37 Yes Travel_Rarely 1141 Research & Development
## 89 89 27 No Travel_Frequently 994 Sales
## 90 90 46 No Travel_Rarely 705 Sales
## 92 92 29 Yes Travel_Rarely 408 Sales
## 93 93 37 Yes Travel_Rarely 1373 Research & Development
## 96 96 42 No Travel_Rarely 635 Sales
## 99 99 29 No Travel_Frequently 410 Research & Development
## 109 109 46 Yes Travel_Rarely 261 Research & Development
## 111 111 28 No Travel_Frequently 773 Research & Development
## 115 115 35 No Travel_Rarely 1361 Sales
## 117 117 25 No Travel_Rarely 1356 Sales
## 130 130 46 No Travel_Rarely 1319 Sales
## 134 134 39 No Travel_Rarely 492 Research & Development
## 137 137 29 Yes Travel_Rarely 805 Research & Development
## 139 139 39 Yes Travel_Rarely 1122 Research & Development
## 140 140 37 No Travel_Rarely 1189 Sales
## 144 144 37 No Travel_Rarely 228 Sales
## 148 148 33 No Travel_Rarely 1242 Sales
## 151 151 31 Yes Travel_Rarely 1365 Sales
## 158 158 39 Yes Travel_Frequently 203 Research & Development
## 167 167 49 No Travel_Rarely 301 Research & Development
## 172 172 47 No Non-Travel 1169 Research & Development
## 173 173 46 No Travel_Frequently 1034 Research & Development
## 175 175 53 No Travel_Rarely 1376 Sales
## 180 180 38 No Travel_Rarely 702 Sales
## 193 193 46 No Travel_Rarely 1485 Research & Development
## 196 196 51 No Travel_Rarely 833 Research & Development
## 197 197 51 No Travel_Rarely 432 Research & Development
## 200 200 24 No Travel_Rarely 823 Research & Development
## 202 202 32 No Travel_Rarely 548 Research & Development
## 209 209 30 No Travel_Rarely 201 Research & Development
## 213 213 37 No Travel_Rarely 1225 Research & Development
## 220 220 26 No Travel_Rarely 583 Research & Development
## 233 233 47 Yes Travel_Frequently 1093 Sales
## 236 236 37 Yes Travel_Rarely 625 Sales
## 239 239 25 No Travel_Rarely 309 Human Resources
## 240 240 50 No Travel_Rarely 813 Research & Development
## 244 244 25 Yes Travel_Rarely 383 Sales
## 246 246 29 No Travel_Rarely 1086 Research & Development
## 257 257 31 No Travel_Frequently 444 Sales
## 261 261 31 Yes Travel_Frequently 703 Sales
## 266 266 35 No Travel_Rarely 809 Research & Development
## 268 268 32 Yes Travel_Rarely 1259 Research & Development
## 273 273 27 No Travel_Rarely 728 Sales
## 282 282 41 No Travel_Rarely 263 Research & Development
## 283 283 40 No Travel_Rarely 1194 Research & Development
## 288 288 34 No Travel_Rarely 1346 Research & Development
## 301 301 32 No Travel_Frequently 379 Sales
## 304 304 35 No Travel_Rarely 1395 Research & Development
## 312 312 40 No Travel_Rarely 302 Research & Development
## 315 315 60 No Travel_Rarely 1179 Sales
## 316 316 47 No Travel_Rarely 955 Sales
## 322 322 34 No Travel_Rarely 1397 Research & Development
## 323 323 30 No Travel_Frequently 1012 Research & Development
## 327 327 44 No Travel_Rarely 1313 Research & Development
## 336 336 33 Yes Travel_Frequently 827 Research & Development
## 337 337 35 No Travel_Frequently 1182 Sales
## 339 339 50 Yes Travel_Frequently 959 Sales
## 342 342 28 No Travel_Frequently 193 Research & Development
## 355 355 41 No Travel_Rarely 796 Sales
## 356 356 34 No Travel_Rarely 1326 Sales
## 359 359 32 No Travel_Rarely 427 Research & Development
## 366 366 56 No Travel_Frequently 906 Sales
## 370 370 32 No Travel_Rarely 1018 Research & Development
## 374 374 37 No Travel_Rarely 1470 Research & Development
## 376 376 50 No Travel_Frequently 333 Research & Development
## 386 386 38 No Travel_Rarely 1333 Research & Development
## 387 387 35 No Travel_Frequently 944 Sales
## 388 388 29 No Travel_Rarely 232 Research & Development
## 396 396 31 No Travel_Rarely 192 Research & Development
## 398 398 34 Yes Travel_Rarely 1107 Human Resources
## 402 402 42 No Non-Travel 926 Research & Development
## 406 406 43 No Travel_Rarely 782 Research & Development
## 410 410 43 No Travel_Rarely 589 Research & Development
## 426 426 43 No Travel_Rarely 415 Sales
## 447 447 38 No Travel_Frequently 1189 Research & Development
## 452 452 44 No Travel_Rarely 1467 Research & Development
## 458 458 51 No Travel_Rarely 632 Sales
## 462 462 36 No Travel_Rarely 311 Research & Development
## 466 466 38 No Travel_Rarely 371 Research & Development
## 473 473 45 No Travel_Rarely 193 Research & Development
## 483 483 34 No Travel_Frequently 735 Research & Development
## 487 487 30 Yes Travel_Rarely 945 Sales
## 493 493 29 No Travel_Rarely 469 Sales
## 502 502 28 Yes Travel_Rarely 103 Research & Development
## 505 505 47 No Travel_Rarely 359 Research & Development
## 514 514 33 No Travel_Frequently 515 Research & Development
## 515 515 21 No Non-Travel 895 Sales
## 521 521 34 No Travel_Frequently 702 Research & Development
## 527 527 45 No Travel_Rarely 1268 Sales
## 531 531 38 No Travel_Rarely 330 Research & Development
## 533 533 29 No Travel_Rarely 1252 Research & Development
## 536 536 43 No Non-Travel 1344 Research & Development
## 544 544 38 No Travel_Rarely 1261 Research & Development
## 556 556 37 No Travel_Rarely 558 Sales
## 568 568 30 Yes Travel_Rarely 740 Sales
## 570 570 50 No Travel_Rarely 939 Research & Development
## 576 576 37 No Travel_Rarely 1115 Research & Development
## 579 579 39 No Travel_Frequently 711 Research & Development
## 584 584 28 No Travel_Rarely 760 Sales
## 603 603 30 No Travel_Rarely 153 Research & Development
## 606 606 53 No Travel_Rarely 970 Research & Development
## 609 609 26 No Travel_Rarely 652 Research & Development
## 611 611 35 No Travel_Rarely 817 Research & Development
## 612 612 41 No Non-Travel 509 Research & Development
## 614 614 19 Yes Travel_Rarely 419 Sales
## 616 616 34 No Travel_Rarely 1157 Research & Development
## 617 617 33 No Travel_Rarely 1099 Research & Development
## 625 625 28 Yes Travel_Frequently 289 Research & Development
## 626 626 27 No Travel_Rarely 1167 Research & Development
## 630 630 34 No Travel_Rarely 1442 Research & Development
## 632 632 34 No Travel_Rarely 937 Sales
## 644 644 34 Yes Non-Travel 967 Research & Development
## 651 651 35 No Travel_Rarely 992 Research & Development
## 655 655 22 Yes Travel_Frequently 1368 Research & Development
## 668 668 33 Yes Travel_Rarely 587 Research & Development
## 669 669 32 Yes Travel_Rarely 1089 Research & Development
## 684 684 57 No Travel_Rarely 334 Research & Development
## 686 686 31 No Travel_Rarely 1274 Research & Development
## 689 689 19 Yes Travel_Rarely 303 Research & Development
## 695 695 37 No Travel_Frequently 1278 Sales
## 703 703 35 No Non-Travel 1212 Sales
## 704 704 26 Yes Travel_Rarely 920 Human Resources
## 706 706 41 No Non-Travel 267 Sales
## 709 709 38 No Travel_Rarely 1153 Research & Development
## 711 711 30 No Travel_Rarely 634 Research & Development
## 717 717 30 No Travel_Rarely 852 Sales
## 718 718 28 No Travel_Rarely 857 Research & Development
## 721 721 37 No Travel_Rarely 482 Research & Development
## 722 722 37 No Travel_Rarely 671 Research & Development
## 724 724 36 No Travel_Rarely 796 Research & Development
## 725 725 42 No Travel_Rarely 1142 Research & Development
## 735 735 34 No Travel_Rarely 479 Research & Development
## 743 743 37 No Travel_Rarely 674 Research & Development
## 746 746 24 No Travel_Rarely 350 Research & Development
## 748 748 34 No Travel_Frequently 669 Research & Development
## 749 749 24 No Travel_Rarely 506 Research & Development
## 750 750 33 No Travel_Rarely 134 Research & Development
## 751 751 20 Yes Travel_Frequently 769 Sales
## 756 756 44 No Non-Travel 381 Research & Development
## 759 759 38 No Travel_Rarely 1495 Research & Development
## 764 764 50 No Non-Travel 145 Sales
## 780 780 29 Yes Travel_Rarely 1092 Research & Development
## 783 783 33 No Travel_Rarely 1075 Human Resources
## 787 787 51 No Travel_Rarely 942 Research & Development
## 791 791 49 No Travel_Rarely 271 Research & Development
## 798 798 22 Yes Travel_Rarely 1294 Research & Development
## 835 835 55 No Non-Travel 177 Research & Development
## 837 837 37 No Travel_Rarely 1439 Research & Development
## 839 839 47 No Travel_Rarely 1001 Research & Development
## 849 849 40 Yes Travel_Rarely 575 Sales
## 851 851 31 Yes Travel_Rarely 542 Sales
## 855 855 35 No Travel_Rarely 219 Research & Development
## 861 861 51 No Travel_Frequently 968 Research & Development
## 867 867 32 No Non-Travel 976 Sales
## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
## 5 2 1 Technical Degree 1 1646
## 8 10 4 Life Sciences 1 1105
## 9 10 4 Life Sciences 1 1055
## 16 20 2 Marketing 1 947
## 18 7 4 Medical 1 487
## 20 1 4 Life Sciences 1 1903
## 26 5 3 Marketing 1 1267
## 44 9 2 Other 1 1595
## 46 9 2 Medical 1 1480
## 47 1 2 Life Sciences 1 1734
## 50 9 3 Medical 1 218
## 52 1 3 Medical 1 1423
## 56 9 4 Life Sciences 1 1772
## 62 10 4 Technical Degree 1 1312
## 69 1 3 Medical 1 88
## 72 5 3 Medical 1 1469
## 73 23 2 Life Sciences 1 261
## 78 1 4 Life Sciences 1 1602
## 80 2 1 Medical 1 1974
## 81 11 2 Medical 1 1033
## 89 8 3 Life Sciences 1 56
## 90 2 4 Marketing 1 38
## 92 23 1 Life Sciences 1 1165
## 93 2 2 Other 1 4
## 96 1 1 Life Sciences 1 387
## 99 2 1 Life Sciences 1 1513
## 109 21 2 Medical 1 1821
## 111 6 3 Life Sciences 1 1154
## 115 17 4 Life Sciences 1 1218
## 117 10 4 Life Sciences 1 1240
## 130 3 3 Technical Degree 1 1863
## 134 12 3 Medical 1 1654
## 137 1 2 Life Sciences 1 816
## 139 6 3 Medical 1 932
## 140 3 3 Life Sciences 1 152
## 144 6 4 Medical 1 378
## 148 8 4 Life Sciences 1 1560
## 151 13 4 Medical 1 650
## 158 2 3 Life Sciences 1 1127
## 167 22 4 Other 1 1655
## 172 14 4 Technical Degree 1 1934
## 173 18 1 Medical 1 624
## 175 2 2 Medical 1 981
## 180 1 4 Life Sciences 1 230
## 193 18 3 Medical 1 550
## 196 1 3 Life Sciences 1 353
## 197 9 4 Life Sciences 1 116
## 200 17 2 Other 1 643
## 202 1 3 Life Sciences 1 96
## 209 5 3 Technical Degree 1 197
## 213 10 2 Life Sciences 1 715
## 220 4 2 Life Sciences 1 1275
## 233 9 3 Life Sciences 1 1716
## 236 1 4 Life Sciences 1 970
## 239 2 3 Human Resources 1 1987
## 240 17 5 Life Sciences 1 1656
## 244 9 2 Life Sciences 1 1439
## 246 7 1 Medical 1 912
## 257 5 3 Marketing 1 399
## 261 2 3 Life Sciences 1 1379
## 266 16 3 Medical 1 14
## 268 2 4 Life Sciences 1 1692
## 273 23 1 Medical 1 1864
## 282 6 3 Medical 1 957
## 283 1 3 Life Sciences 1 1822
## 288 19 2 Medical 1 18
## 301 5 2 Life Sciences 1 889
## 304 9 4 Medical 1 2008
## 312 6 3 Life Sciences 1 601
## 315 16 4 Marketing 1 732
## 316 4 2 Life Sciences 1 1003
## 322 1 5 Life Sciences 1 683
## 323 5 4 Life Sciences 1 861
## 327 7 3 Medical 1 1608
## 336 29 4 Medical 1 1101
## 337 11 2 Marketing 1 1137
## 339 1 4 Other 1 1113
## 342 2 3 Life Sciences 1 1296
## 355 4 1 Marketing 1 1815
## 356 3 3 Other 1 1478
## 359 1 3 Medical 1 78
## 366 6 3 Life Sciences 1 532
## 370 2 4 Medical 1 439
## 374 10 3 Medical 1 1640
## 376 22 5 Medical 1 1539
## 386 1 3 Technical Degree 1 950
## 387 1 3 Marketing 1 314
## 388 19 3 Technical Degree 1 611
## 396 2 4 Life Sciences 1 426
## 398 9 4 Technical Degree 1 1467
## 402 21 2 Medical 1 270
## 406 6 4 Other 1 661
## 410 14 2 Life Sciences 1 843
## 426 25 3 Medical 1 1076
## 447 1 3 Life Sciences 1 1668
## 452 20 3 Life Sciences 1 1475
## 458 21 4 Marketing 1 120
## 462 7 3 Life Sciences 1 1659
## 466 2 3 Life Sciences 1 24
## 473 6 4 Other 1 101
## 483 22 4 Other 1 1932
## 487 9 3 Medical 1 1876
## 493 10 3 Medical 1 1650
## 502 24 3 Life Sciences 1 19
## 505 2 4 Medical 1 1443
## 514 1 2 Life Sciences 1 73
## 515 9 2 Medical 1 484
## 521 16 4 Life Sciences 1 838
## 527 4 2 Life Sciences 1 240
## 531 17 1 Life Sciences 1 1088
## 533 23 2 Life Sciences 1 824
## 536 7 3 Medical 1 262
## 544 2 4 Life Sciences 1 271
## 556 2 3 Marketing 1 656
## 568 1 3 Life Sciences 1 1562
## 570 24 3 Life Sciences 1 1005
## 576 1 4 Life Sciences 1 77
## 579 4 3 Medical 1 1633
## 584 2 4 Marketing 1 846
## 603 8 2 Life Sciences 1 1015
## 606 7 3 Life Sciences 1 730
## 609 7 3 Other 1 1417
## 611 1 3 Medical 1 1369
## 612 2 4 Other 1 616
## 614 21 3 Other 1 959
## 616 5 2 Medical 1 1696
## 617 4 4 Medical 1 1502
## 625 2 2 Medical 1 1504
## 626 4 2 Life Sciences 1 1259
## 630 9 3 Medical 1 717
## 632 1 3 Marketing 1 1950
## 644 16 4 Technical Degree 1 1905
## 651 1 3 Medical 1 1564
## 655 4 1 Technical Degree 1 593
## 668 10 1 Medical 1 584
## 669 7 2 Life Sciences 1 1309
## 684 24 2 Life Sciences 1 223
## 686 9 1 Life Sciences 1 581
## 689 2 3 Life Sciences 1 243
## 695 1 4 Medical 1 1700
## 703 8 2 Marketing 1 1243
## 704 20 2 Medical 1 1818
## 706 10 2 Life Sciences 1 599
## 709 6 2 Other 1 1782
## 711 17 4 Medical 1 1321
## 717 10 3 Marketing 1 1179
## 718 10 3 Other 1 1097
## 721 3 3 Other 1 689
## 722 19 3 Life Sciences 1 1631
## 724 12 5 Medical 1 1073
## 725 8 3 Life Sciences 1 1860
## 735 7 4 Medical 1 1577
## 743 13 3 Medical 1 1543
## 746 21 2 Technical Degree 1 1551
## 748 1 3 Medical 1 1184
## 749 29 1 Medical 1 1725
## 750 2 3 Life Sciences 1 242
## 751 9 3 Marketing 1 1077
## 756 24 3 Medical 1 744
## 759 4 2 Medical 1 1687
## 764 1 3 Life Sciences 1 1040
## 780 1 4 Medical 1 2027
## 783 3 2 Human Resources 1 910
## 787 3 3 Technical Degree 1 1786
## 791 3 2 Medical 1 1509
## 798 8 1 Medical 1 1783
## 835 8 1 Medical 1 1278
## 837 4 1 Life Sciences 1 1394
## 839 4 3 Life Sciences 1 1827
## 849 22 2 Marketing 1 492
## 851 20 3 Life Sciences 1 175
## 855 16 2 Other 1 1886
## 861 6 2 Medical 1 1297
## 867 26 4 Marketing 1 333
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 5 1 Female 32 3 1
## 8 4 Female 88 2 2
## 9 3 Female 87 3 1
## 16 4 Female 45 3 2
## 18 3 Female 30 3 2
## 20 2 Male 78 4 2
## 26 2 Male 88 3 5
## 44 4 Male 93 2 5
## 46 1 Female 74 3 1
## 47 1 Male 34 1 2
## 50 4 Male 57 3 1
## 52 4 Female 76 3 5
## 56 3 Male 94 3 1
## 62 4 Female 63 3 3
## 69 2 Male 79 3 1
## 72 4 Male 84 3 2
## 73 2 Male 50 2 2
## 78 4 Male 56 4 4
## 80 4 Female 59 1 1
## 81 1 Female 61 1 2
## 89 4 Male 37 3 3
## 90 2 Female 83 3 5
## 92 4 Female 45 2 3
## 93 4 Male 92 2 1
## 96 2 Male 99 3 2
## 99 4 Female 97 3 1
## 109 4 Female 66 3 2
## 111 3 Male 39 2 1
## 115 3 Male 94 3 2
## 117 3 Male 57 3 2
## 130 1 Female 45 4 4
## 134 4 Male 66 3 2
## 137 2 Female 36 2 1
## 139 4 Male 70 3 1
## 140 3 Male 87 3 3
## 144 3 Male 98 3 2
## 148 1 Male 46 3 2
## 151 2 Male 46 3 2
## 158 1 Male 84 3 4
## 167 1 Female 72 3 4
## 172 3 Male 64 3 2
## 173 1 Female 86 3 3
## 175 3 Male 45 3 4
## 180 1 Female 59 2 2
## 193 3 Female 87 3 2
## 196 3 Male 96 3 1
## 197 4 Male 96 3 1
## 200 4 Male 94 2 1
## 202 2 Male 66 3 2
## 209 4 Female 84 3 1
## 213 4 Male 80 4 1
## 220 3 Male 53 3 1
## 233 3 Male 82 1 4
## 236 1 Male 46 2 3
## 239 3 Female 82 3 1
## 240 4 Female 50 2 3
## 244 1 Male 68 2 1
## 246 1 Female 62 2 1
## 257 4 Female 84 3 1
## 261 3 Female 90 2 1
## 266 1 Male 84 4 1
## 268 4 Male 95 3 1
## 273 2 Female 36 2 2
## 282 4 Male 59 3 1
## 283 3 Female 52 3 2
## 288 2 Male 93 3 1
## 301 2 Male 48 3 2
## 304 2 Male 48 3 2
## 312 2 Female 75 3 4
## 315 1 Male 84 3 2
## 316 4 Female 83 3 2
## 322 2 Male 42 3 1
## 323 2 Male 75 2 1
## 327 2 Female 31 3 5
## 336 1 Female 54 2 2
## 337 4 Male 54 3 2
## 339 4 Male 81 3 2
## 342 4 Male 52 2 1
## 355 3 Female 81 3 3
## 356 4 Male 81 1 2
## 359 1 Male 33 3 2
## 366 3 Female 86 4 4
## 370 1 Female 74 4 2
## 374 2 Female 71 3 1
## 376 3 Male 88 1 4
## 386 4 Female 80 3 3
## 387 3 Female 92 3 3
## 388 4 Male 34 3 2
## 396 3 Male 32 3 1
## 398 1 Female 52 3 1
## 402 3 Female 36 3 2
## 406 2 Male 50 2 4
## 410 2 Male 94 3 4
## 426 3 Male 79 2 3
## 447 4 Male 90 3 2
## 452 4 Male 49 3 1
## 458 3 Male 71 3 2
## 462 1 Male 77 3 1
## 466 4 Male 45 3 1
## 473 4 Male 52 3 3
## 483 3 Male 86 2 2
## 487 2 Male 89 3 1
## 493 3 Male 42 2 2
## 502 3 Male 50 2 1
## 505 1 Female 82 3 4
## 514 1 Female 98 3 3
## 515 1 Male 39 3 1
## 521 3 Female 100 2 1
## 527 3 Female 30 3 2
## 531 3 Female 65 2 3
## 533 3 Male 81 4 1
## 536 4 Male 37 4 1
## 544 4 Male 88 3 2
## 556 4 Male 75 3 2
## 568 2 Male 64 2 2
## 570 4 Male 95 3 4
## 576 1 Male 51 2 2
## 579 1 Female 81 3 2
## 584 2 Female 81 3 2
## 603 2 Female 73 4 3
## 606 3 Male 59 4 4
## 609 3 Male 100 4 1
## 611 4 Female 60 2 2
## 612 1 Female 62 2 2
## 614 4 Male 37 2 1
## 616 2 Male 57 2 2
## 617 1 Female 82 2 1
## 625 3 Male 38 2 1
## 626 1 Male 76 3 1
## 630 4 Female 46 2 3
## 632 1 Male 32 3 3
## 644 4 Male 85 1 1
## 651 4 Male 68 2 1
## 655 3 Male 99 2 1
## 668 1 Male 38 1 1
## 669 4 Male 79 3 2
## 684 3 Male 83 4 3
## 686 3 Male 33 3 3
## 689 2 Male 47 2 1
## 695 3 Male 31 1 2
## 703 3 Female 78 2 3
## 704 4 Female 69 3 1
## 706 4 Male 56 3 2
## 709 4 Female 40 2 1
## 711 2 Female 95 3 3
## 717 3 Male 72 2 2
## 718 3 Female 59 3 2
## 721 3 Male 36 3 3
## 722 3 Male 85 3 2
## 724 4 Female 51 2 3
## 725 4 Male 81 3 1
## 735 1 Male 35 3 1
## 743 1 Male 47 3 2
## 746 3 Male 57 2 1
## 748 4 Male 97 2 2
## 749 2 Male 91 3 1
## 750 3 Male 90 3 1
## 751 4 Female 54 3 1
## 756 1 Male 49 1 1
## 759 4 Female 87 3 1
## 764 4 Female 95 3 2
## 780 1 Male 36 3 1
## 783 4 Male 57 3 1
## 787 1 Female 53 3 3
## 791 3 Female 43 2 2
## 798 3 Female 79 3 1
## 835 4 Male 37 2 4
## 837 3 Male 54 3 1
## 839 3 Female 92 2 3
## 849 3 Male 68 2 2
## 851 2 Female 71 1 2
## 855 4 Female 44 2 2
## 861 2 Female 40 2 1
## 867 3 Male 100 3 2
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 5 Research Scientist 4 Single 3760
## 8 Sales Executive 4 Divorced 6694
## 9 Sales Representative 3 Married 2220
## 16 Sales Executive 3 Married 6932
## 18 Manufacturing Director 3 Married 5258
## 20 Healthcare Representative 1 Married 5033
## 26 Manager 2 Married 18213
## 44 Manager 4 Single 19613
## 46 Laboratory Technician 4 Single 3673
## 47 Sales Executive 2 Single 6735
## 50 Research Scientist 2 Divorced 2024
## 52 Research Director 1 Married 19701
## 56 Laboratory Technician 1 Divorced 2289
## 62 Healthcare Representative 3 Married 7094
## 69 Research Scientist 1 Married 2194
## 72 Sales Executive 1 Single 8463
## 73 Manufacturing Director 3 Married 5916
## 78 Manager 1 Single 17567
## 80 Laboratory Technician 4 Divorced 1129
## 81 Healthcare Representative 2 Married 4777
## 89 Sales Executive 3 Single 8726
## 90 Manager 1 Single 18947
## 92 Sales Executive 1 Married 7336
## 93 Laboratory Technician 3 Single 2090
## 96 Sales Executive 3 Married 4907
## 99 Laboratory Technician 2 Married 3180
## 109 Healthcare Representative 2 Married 8926
## 111 Research Scientist 3 Divorced 2703
## 115 Sales Executive 1 Married 8966
## 117 Sales Executive 4 Single 4950
## 130 Sales Executive 1 Divorced 13225
## 134 Manufacturing Director 2 Married 5295
## 137 Laboratory Technician 1 Married 2319
## 139 Laboratory Technician 1 Married 2404
## 140 Sales Executive 4 Single 7428
## 144 Sales Executive 4 Married 6502
## 148 Sales Executive 1 Married 6392
## 151 Sales Executive 1 Divorced 4233
## 158 Healthcare Representative 4 Divorced 12169
## 167 Research Director 2 Married 16413
## 172 Research Scientist 2 Married 5467
## 173 Healthcare Representative 3 Married 10527
## 175 Manager 3 Divorced 14852
## 180 Sales Executive 4 Single 8686
## 193 Manufacturing Director 3 Divorced 4810
## 196 Research Scientist 4 Married 2723
## 197 Laboratory Technician 4 Married 2075
## 200 Laboratory Technician 2 Married 2127
## 202 Research Scientist 2 Married 6220
## 209 Research Scientist 1 Divorced 3204
## 213 Research Scientist 4 Single 4680
## 220 Research Scientist 4 Single 2875
## 233 Sales Executive 3 Married 12936
## 236 Sales Executive 3 Married 10609
## 239 Human Resources 2 Married 2187
## 240 Research Director 1 Divorced 13269
## 244 Sales Representative 1 Married 4400
## 246 Laboratory Technician 4 Divorced 2532
## 257 Sales Representative 2 Divorced 2789
## 261 Sales Representative 4 Single 2785
## 266 Laboratory Technician 2 Married 2426
## 268 Laboratory Technician 2 Single 1393
## 273 Sales Representative 3 Married 3540
## 282 Laboratory Technician 1 Single 4721
## 283 Healthcare Representative 4 Divorced 6513
## 288 Laboratory Technician 4 Divorced 2661
## 301 Sales Executive 2 Married 6524
## 304 Research Scientist 3 Single 5098
## 312 Manufacturing Director 3 Single 13237
## 315 Sales Executive 1 Single 5405
## 316 Sales Executive 4 Single 4163
## 322 Research Scientist 4 Married 2691
## 323 Research Scientist 4 Divorced 3761
## 327 Research Director 4 Divorced 19049
## 336 Research Scientist 3 Single 4508
## 337 Sales Executive 4 Divorced 4968
## 339 Sales Executive 3 Single 4728
## 342 Laboratory Technician 4 Married 3867
## 355 Sales Executive 3 Divorced 10447
## 356 Sales Executive 1 Single 4759
## 359 Manufacturing Director 4 Married 6162
## 366 Sales Executive 1 Married 13212
## 370 Research Scientist 4 Single 5055
## 374 Research Scientist 2 Married 3936
## 376 Research Director 4 Single 14411
## 386 Research Director 1 Married 13582
## 387 Sales Executive 3 Single 8789
## 388 Manufacturing Director 4 Divorced 4262
## 396 Research Scientist 4 Divorced 2695
## 398 Human Resources 3 Married 2742
## 402 Manufacturing Director 3 Divorced 5265
## 406 Research Director 4 Divorced 16627
## 410 Research Director 1 Married 17159
## 426 Sales Executive 4 Divorced 10798
## 447 Research Scientist 4 Married 4735
## 452 Research Scientist 2 Single 3420
## 458 Sales Executive 4 Single 5441
## 462 Laboratory Technician 2 Single 2013
## 466 Research Scientist 4 Single 3944
## 473 Research Director 1 Married 13245
## 483 Research Scientist 4 Married 5747
## 487 Sales Representative 4 Single 1081
## 493 Sales Executive 3 Single 5869
## 502 Laboratory Technician 3 Single 2028
## 505 Research Director 3 Married 17169
## 514 Research Director 4 Single 13458
## 515 Sales Representative 4 Single 2610
## 521 Research Scientist 4 Single 2553
## 527 Sales Executive 1 Divorced 5006
## 531 Healthcare Representative 3 Married 8823
## 533 Research Scientist 3 Married 2700
## 536 Research Scientist 4 Divorced 2089
## 544 Manufacturing Director 3 Married 6553
## 556 Sales Executive 3 Married 9602
## 568 Sales Executive 1 Married 9714
## 570 Manufacturing Director 3 Married 13973
## 576 Manufacturing Director 3 Divorced 5993
## 579 Manufacturing Director 3 Single 5042
## 584 Sales Executive 2 Married 4779
## 603 Research Director 1 Married 11416
## 606 Research Director 3 Married 14814
## 609 Laboratory Technician 1 Single 3578
## 611 Laboratory Technician 4 Married 5363
## 612 Healthcare Representative 3 Single 6811
## 614 Sales Representative 2 Single 2121
## 616 Laboratory Technician 4 Married 3986
## 617 Laboratory Technician 2 Married 3838
## 625 Laboratory Technician 1 Single 2561
## 626 Research Scientist 3 Divorced 2517
## 630 Healthcare Representative 2 Single 8621
## 632 Sales Executive 4 Single 9888
## 644 Research Scientist 1 Married 2307
## 651 Laboratory Technician 1 Single 2450
## 655 Laboratory Technician 3 Single 3894
## 668 Laboratory Technician 4 Divorced 3408
## 669 Laboratory Technician 3 Married 4883
## 684 Healthcare Representative 4 Divorced 9439
## 686 Manufacturing Director 2 Divorced 10648
## 689 Laboratory Technician 4 Single 1102
## 695 Sales Executive 4 Divorced 9525
## 703 Sales Executive 4 Married 10377
## 704 Human Resources 2 Married 2148
## 706 Sales Executive 4 Single 6230
## 709 Laboratory Technician 3 Married 3702
## 711 Manager 1 Married 11916
## 717 Sales Executive 3 Married 6578
## 718 Research Scientist 3 Single 3660
## 721 Manufacturing Director 3 Married 9434
## 722 Manufacturing Director 3 Married 5768
## 724 Manufacturing Director 4 Single 8858
## 725 Laboratory Technician 3 Single 3968
## 735 Research Scientist 4 Single 2972
## 743 Research Scientist 4 Married 4285
## 746 Laboratory Technician 1 Divorced 2296
## 748 Healthcare Representative 1 Single 5343
## 749 Laboratory Technician 1 Divorced 3907
## 750 Research Scientist 4 Single 2500
## 751 Sales Representative 4 Single 2323
## 756 Laboratory Technician 3 Single 3708
## 759 Laboratory Technician 3 Married 3306
## 764 Sales Executive 3 Married 6347
## 780 Research Scientist 4 Married 4787
## 783 Human Resources 2 Divorced 2277
## 787 Manager 3 Married 13116
## 791 Laboratory Technician 1 Married 4789
## 798 Laboratory Technician 1 Married 2398
## 835 Healthcare Representative 2 Divorced 13577
## 837 Research Scientist 3 Married 2996
## 839 Manufacturing Director 2 Divorced 10333
## 849 Sales Executive 3 Married 6380
## 851 Sales Executive 3 Married 4559
## 855 Manufacturing Director 2 Married 4788
## 861 Laboratory Technician 3 Single 2838
## 867 Sales Executive 4 Married 4465
## MonthlyRate NumCompaniesWorked OverTime PercentSalaryHike PerformanceRating
## 5 17218 1 Yes 13 3
## 8 24223 2 Yes 14 3
## 9 18410 1 Yes 19 3
## 16 24406 1 No 13 3
## 18 16044 2 No 14 3
## 20 9364 2 No 15 3
## 26 8751 7 No 11 3
## 44 26362 8 No 22 4
## 46 16458 1 No 13 3
## 47 12147 6 No 15 3
## 50 5970 6 No 18 3
## 52 22456 3 Yes 21 4
## 56 20520 1 No 20 4
## 62 5747 3 No 12 3
## 69 5868 4 No 13 3
## 72 23490 0 No 18 3
## 73 15497 3 Yes 13 3
## 78 3156 1 No 15 3
## 80 17536 1 Yes 11 3
## 81 14382 5 No 15 3
## 89 2975 1 No 15 3
## 90 22822 3 No 12 3
## 92 11162 1 No 13 3
## 93 2396 6 Yes 15 3
## 96 24532 1 No 25 4
## 99 4668 0 No 13 3
## 109 10842 4 No 22 4
## 111 22088 1 Yes 14 3
## 115 21026 3 Yes 15 3
## 117 20623 0 No 14 3
## 130 7739 2 No 12 3
## 134 7693 4 No 21 4
## 137 6689 1 Yes 11 3
## 139 4303 7 Yes 21 4
## 140 14506 2 No 12 3
## 144 22825 4 No 14 3
## 148 10589 2 No 13 3
## 151 11512 2 No 17 3
## 158 13547 7 No 11 3
## 167 3498 3 No 16 3
## 172 2125 8 No 18 3
## 173 8984 5 No 11 3
## 175 13938 6 No 13 3
## 180 12930 4 No 22 4
## 193 26314 2 No 14 3
## 196 23231 1 No 11 3
## 197 18725 3 No 23 4
## 200 9100 1 No 21 4
## 202 7346 1 No 17 3
## 209 10415 5 No 14 3
## 213 15232 3 No 17 3
## 220 9973 1 Yes 20 4
## 233 24164 7 No 11 3
## 236 14922 5 No 11 3
## 239 19655 4 No 14 3
## 240 21981 5 No 15 3
## 244 15182 3 No 12 3
## 246 6054 6 No 14 3
## 257 3909 1 No 11 3
## 261 11882 7 No 14 3
## 266 16479 0 No 13 3
## 268 24852 1 No 12 3
## 273 7018 1 No 21 4
## 282 3119 2 Yes 13 3
## 283 9060 4 No 17 3
## 288 8758 0 No 11 3
## 301 8891 1 No 14 3
## 304 18698 1 No 19 3
## 312 20364 7 No 15 3
## 315 11924 8 No 14 3
## 316 8571 1 Yes 17 3
## 322 7660 1 No 12 3
## 323 2373 9 No 12 3
## 327 3549 0 Yes 14 3
## 336 3129 1 No 22 4
## 337 18500 1 No 11 3
## 339 17251 3 Yes 14 3
## 342 14222 1 Yes 12 3
## 355 26458 0 Yes 13 3
## 356 15891 3 No 18 3
## 359 10877 1 Yes 22 4
## 366 18256 9 No 11 3
## 370 10557 7 No 16 3
## 374 9953 1 No 11 3
## 376 24450 1 Yes 13 3
## 386 16292 1 No 13 3
## 387 9096 1 No 14 3
## 388 22645 4 No 12 3
## 396 7747 0 Yes 18 3
## 398 3072 1 No 15 3
## 402 16439 2 No 16 3
## 406 2671 4 Yes 14 3
## 410 5200 6 No 24 4
## 426 5268 5 No 13 3
## 447 9867 7 No 15 3
## 452 21158 1 No 13 3
## 458 8423 0 Yes 22 4
## 462 10950 2 No 11 3
## 466 4306 5 Yes 11 3
## 473 15067 4 Yes 14 3
## 483 26496 1 Yes 15 3
## 487 16019 1 No 13 3
## 493 23413 9 No 11 3
## 502 12947 5 Yes 14 3
## 505 26703 3 No 19 3
## 514 15146 1 Yes 12 3
## 515 2851 1 No 24 4
## 521 8306 1 No 16 3
## 527 6319 4 Yes 11 3
## 531 24608 0 No 18 3
## 533 23779 1 No 24 4
## 536 5228 4 No 14 3
## 544 7259 9 No 14 3
## 556 3010 4 Yes 11 3
## 568 5323 1 No 11 3
## 570 4161 3 Yes 18 3
## 576 2689 1 No 18 3
## 579 3140 0 No 13 3
## 584 3698 1 Yes 20 4
## 603 17802 0 Yes 12 3
## 606 13514 3 No 19 3
## 609 23577 0 No 12 3
## 611 10846 0 No 12 3
## 612 2112 2 Yes 17 3
## 614 9947 1 Yes 13 3
## 616 11912 1 No 14 3
## 617 8192 8 No 11 3
## 625 5355 7 No 11 3
## 626 3208 1 No 11 3
## 630 17654 1 No 14 3
## 632 6770 1 No 21 4
## 644 14460 1 Yes 23 4
## 651 21731 1 No 19 3
## 655 9129 5 No 16 3
## 668 6705 7 No 13 3
## 669 22845 1 No 18 3
## 684 23402 3 Yes 16 3
## 686 14394 1 No 25 4
## 689 9241 1 No 22 4
## 695 7677 1 No 14 3
## 703 13755 4 Yes 11 3
## 704 6889 0 Yes 11 3
## 706 13430 7 No 14 3
## 709 16376 1 No 11 3
## 711 25927 1 Yes 23 4
## 717 2706 1 No 18 3
## 718 7909 3 No 13 3
## 721 9606 1 No 15 3
## 722 26493 3 No 17 3
## 724 15669 0 No 11 3
## 725 13624 4 No 13 3
## 735 22061 1 No 13 3
## 743 3031 1 No 17 3
## 746 10036 0 No 14 3
## 748 25755 0 No 20 4
## 749 3622 1 No 13 3
## 750 10515 0 No 14 3
## 751 17205 1 Yes 14 3
## 756 2104 2 No 14 3
## 759 26176 7 No 19 3
## 764 24920 0 No 12 3
## 780 26124 9 Yes 14 3
## 783 22650 3 Yes 11 3
## 787 22984 2 No 11 3
## 791 23070 4 No 25 4
## 798 15999 1 Yes 17 3
## 835 25592 1 Yes 15 3
## 837 5182 7 Yes 15 3
## 839 19271 8 Yes 12 3
## 849 6110 2 Yes 12 3
## 851 24788 3 Yes 11 3
## 855 25388 0 Yes 11 3
## 861 4257 0 No 14 3
## 867 12069 0 No 18 3
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## 5 3 80 0 6
## 8 3 80 3 8
## 9 4 80 1 1
## 16 4 80 1 9
## 18 3 80 0 7
## 20 4 80 1 10
## 26 3 80 1 26
## 44 4 80 0 24
## 46 3 80 0 12
## 47 2 80 0 10
## 50 4 80 1 6
## 52 3 80 1 32
## 56 2 80 2 5
## 62 1 80 0 10
## 69 4 80 1 5
## 72 4 80 0 6
## 73 1 80 0 8
## 78 2 80 0 27
## 80 3 80 3 1
## 81 1 80 0 15
## 89 4 80 0 9
## 90 4 80 0 22
## 92 1 80 1 11
## 93 2 80 0 7
## 96 3 80 0 20
## 99 3 80 3 4
## 109 4 80 1 13
## 111 4 80 1 3
## 115 4 80 3 15
## 117 2 80 0 5
## 130 4 80 1 25
## 134 3 80 0 7
## 137 4 80 1 1
## 139 4 80 0 8
## 140 1 80 0 12
## 144 2 80 1 7
## 148 4 80 1 8
## 151 3 80 0 9
## 158 4 80 3 21
## 167 2 80 2 27
## 172 3 80 1 16
## 173 4 80 0 28
## 175 3 80 1 22
## 180 3 80 0 12
## 193 3 80 1 19
## 196 2 80 0 1
## 197 2 80 2 10
## 200 4 80 1 1
## 202 2 80 2 10
## 209 4 80 1 8
## 213 1 80 0 4
## 220 2 80 0 8
## 233 3 80 0 25
## 236 3 80 0 17
## 239 3 80 0 6
## 240 3 80 3 19
## 244 1 80 0 6
## 246 3 80 3 8
## 257 3 80 1 2
## 261 3 80 0 3
## 266 3 80 1 6
## 268 1 80 0 1
## 273 4 80 1 9
## 282 3 80 0 20
## 283 4 80 1 12
## 288 3 80 1 3
## 301 4 80 1 10
## 304 2 80 0 10
## 312 3 80 0 22
## 315 4 80 0 10
## 316 3 80 0 9
## 322 4 80 1 10
## 323 2 80 1 10
## 327 4 80 1 23
## 336 2 80 0 14
## 337 4 80 1 5
## 339 4 80 0 5
## 342 2 80 1 2
## 355 4 80 1 23
## 356 4 80 0 15
## 359 2 80 1 9
## 366 4 80 3 36
## 370 3 80 0 10
## 374 1 80 1 8
## 376 4 80 0 32
## 386 2 80 1 15
## 387 1 80 0 10
## 388 2 80 2 8
## 396 2 80 1 3
## 398 4 80 0 2
## 402 2 80 1 11
## 406 3 80 1 21
## 410 3 80 1 22
## 426 3 80 1 18
## 447 4 80 2 19
## 452 3 80 0 6
## 458 4 80 0 11
## 462 3 80 0 15
## 466 3 80 0 6
## 473 2 80 0 17
## 483 2 80 0 16
## 487 3 80 0 1
## 493 3 80 0 8
## 502 2 80 0 6
## 505 2 80 2 26
## 514 3 80 0 15
## 515 3 80 0 3
## 521 3 80 0 6
## 527 1 80 1 9
## 531 1 80 1 20
## 533 3 80 1 10
## 536 4 80 3 7
## 544 2 80 0 14
## 556 3 80 1 17
## 568 4 80 1 10
## 570 4 80 1 22
## 576 3 80 1 7
## 579 4 80 0 10
## 584 1 80 0 8
## 603 3 80 3 9
## 606 3 80 0 32
## 609 4 80 0 8
## 611 2 80 1 10
## 612 1 80 0 10
## 614 2 80 0 1
## 616 3 80 1 15
## 617 4 80 0 8
## 625 3 80 0 8
## 626 2 80 3 5
## 630 2 80 0 9
## 632 1 80 0 14
## 644 2 80 1 5
## 651 2 80 0 3
## 655 3 80 0 4
## 668 1 80 3 8
## 669 1 80 1 10
## 684 2 80 1 12
## 686 4 80 1 13
## 689 3 80 0 1
## 695 3 80 2 6
## 703 2 80 1 16
## 704 3 80 0 6
## 706 4 80 0 16
## 709 2 80 1 5
## 711 4 80 2 9
## 717 1 80 1 10
## 718 4 80 0 10
## 721 3 80 1 10
## 722 1 80 3 9
## 724 2 80 0 15
## 725 4 80 0 8
## 735 3 80 0 1
## 743 1 80 0 10
## 746 2 80 3 2
## 748 3 80 0 14
## 749 2 80 3 6
## 750 1 80 0 4
## 751 2 80 0 2
## 756 3 80 0 9
## 759 4 80 1 7
## 764 1 80 1 19
## 780 2 80 3 4
## 783 3 80 1 7
## 787 4 80 0 15
## 791 1 80 1 10
## 798 3 80 0 1
## 835 4 80 1 34
## 837 4 80 0 8
## 839 3 80 1 28
## 849 1 80 2 8
## 851 3 80 1 4
## 855 4 80 0 4
## 861 2 80 0 8
## 867 1 80 0 4
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 5 2 3 6 3
## 8 5 3 1 0
## 9 2 3 1 1
## 16 2 2 9 8
## 18 2 4 1 0
## 20 5 3 2 0
## 26 5 3 22 9
## 44 2 3 1 0
## 46 3 3 12 9
## 47 2 3 0 0
## 50 1 1 2 2
## 52 3 3 9 8
## 56 2 3 5 3
## 62 0 3 7 7
## 69 2 2 3 2
## 72 4 3 5 4
## 73 1 3 1 0
## 78 5 1 26 0
## 80 4 3 1 0
## 81 2 1 1 0
## 89 0 3 9 8
## 90 2 2 2 2
## 92 3 1 11 8
## 93 3 3 0 0
## 96 3 3 20 16
## 99 3 3 3 2
## 109 2 4 9 7
## 111 2 3 3 1
## 115 2 3 7 7
## 117 4 3 4 3
## 130 5 3 19 17
## 134 3 3 5 4
## 137 1 3 1 0
## 139 2 1 2 2
## 140 3 3 5 3
## 144 5 4 5 4
## 148 6 1 2 2
## 151 2 1 3 1
## 158 4 3 18 7
## 167 2 3 4 2
## 172 4 4 8 7
## 173 3 2 2 2
## 175 3 4 17 13
## 180 2 4 8 3
## 193 5 2 10 7
## 196 0 2 1 0
## 197 4 3 4 2
## 200 2 3 1 0
## 202 3 3 10 4
## 209 3 3 3 2
## 213 2 3 1 0
## 220 2 2 8 5
## 233 3 1 23 5
## 236 2 1 14 1
## 239 3 3 2 0
## 240 3 3 14 11
## 244 2 3 3 2
## 246 5 3 4 3
## 257 5 2 2 2
## 261 3 4 1 0
## 266 5 3 5 4
## 268 2 3 1 0
## 273 5 3 9 8
## 282 3 3 18 13
## 283 3 3 5 3
## 288 2 3 2 2
## 301 3 3 10 8
## 304 5 3 10 7
## 312 3 3 20 6
## 315 1 3 2 2
## 316 0 3 9 0
## 322 4 2 10 9
## 323 3 2 5 4
## 327 4 2 22 7
## 336 4 3 13 7
## 337 3 3 5 2
## 339 4 3 0 0
## 342 2 3 2 2
## 355 3 4 22 14
## 356 2 3 13 9
## 359 3 3 9 8
## 366 0 2 7 7
## 370 0 2 7 7
## 374 2 1 8 4
## 376 2 3 32 6
## 386 3 3 15 12
## 387 3 4 10 7
## 388 2 4 3 2
## 396 2 1 2 2
## 398 0 3 2 2
## 402 5 3 5 3
## 406 3 2 1 0
## 410 3 3 4 1
## 426 5 3 1 0
## 447 4 4 13 11
## 452 3 2 5 2
## 458 2 1 10 7
## 462 4 3 4 3
## 466 3 3 3 2
## 473 3 4 0 0
## 483 3 3 15 10
## 487 3 2 1 0
## 493 2 3 5 2
## 502 4 3 4 2
## 505 2 4 20 17
## 514 1 3 15 14
## 515 3 2 3 2
## 521 3 3 5 2
## 527 3 4 5 4
## 531 4 2 19 9
## 533 3 3 10 7
## 536 3 4 5 4
## 544 3 3 1 0
## 556 3 2 3 0
## 568 4 3 10 8
## 570 2 3 12 11
## 576 2 4 7 5
## 579 2 1 9 2
## 584 2 3 8 7
## 603 4 2 8 7
## 606 3 3 5 1
## 609 2 3 7 7
## 611 0 3 9 7
## 612 3 3 8 7
## 614 3 4 1 0
## 616 3 4 15 10
## 617 5 3 5 4
## 625 2 2 0 0
## 626 2 3 5 3
## 630 3 4 8 7
## 632 3 2 14 8
## 644 2 3 5 2
## 651 3 3 3 0
## 655 3 3 2 2
## 668 2 3 4 3
## 669 3 3 10 4
## 684 2 1 5 3
## 686 6 4 13 8
## 689 3 2 1 0
## 695 2 2 6 3
## 703 6 2 13 2
## 704 3 3 5 1
## 706 3 3 14 3
## 709 3 3 5 4
## 711 2 3 9 1
## 717 3 3 10 3
## 718 4 4 8 7
## 721 2 3 10 7
## 722 2 2 4 3
## 724 2 2 14 8
## 725 3 3 0 0
## 735 4 1 1 0
## 743 2 3 10 8
## 746 3 3 1 1
## 748 3 3 13 9
## 749 2 4 6 2
## 750 2 4 3 1
## 751 3 3 2 2
## 756 5 3 5 2
## 759 5 2 0 0
## 764 3 3 18 7
## 780 3 4 2 2
## 783 4 4 4 3
## 787 2 3 2 2
## 791 3 3 3 2
## 798 6 3 1 0
## 835 3 3 33 9
## 837 2 3 6 4
## 839 4 3 22 11
## 849 6 3 6 4
## 851 2 3 2 2
## 855 2 3 3 2
## 861 6 2 7 0
## 867 2 3 3 2
## YearsSinceLastPromotion YearsWithCurrManager
## 5 1 3
## 8 0 0
## 9 0 0
## 16 0 0
## 18 0 0
## 20 2 2
## 26 3 10
## 44 0 1
## 46 5 8
## 47 0 0
## 50 2 2
## 52 1 5
## 56 0 4
## 62 1 7
## 69 1 2
## 72 1 3
## 73 0 1
## 78 0 12
## 80 0 0
## 81 0 0
## 89 1 7
## 90 2 1
## 92 3 10
## 93 0 0
## 96 11 6
## 99 0 2
## 109 3 7
## 111 0 2
## 115 1 7
## 117 1 1
## 130 2 8
## 134 1 0
## 137 0 0
## 139 2 2
## 140 1 3
## 144 0 1
## 148 2 2
## 151 1 2
## 158 11 5
## 167 1 2
## 172 1 7
## 173 1 2
## 175 15 2
## 180 0 7
## 193 0 8
## 196 0 0
## 197 0 3
## 200 0 0
## 202 0 9
## 209 2 2
## 213 0 0
## 220 2 2
## 233 14 10
## 236 11 7
## 239 1 2
## 240 1 11
## 244 2 2
## 246 0 3
## 257 2 2
## 261 0 0
## 266 0 3
## 268 0 0
## 273 5 8
## 282 2 17
## 283 0 3
## 288 1 2
## 301 5 3
## 304 0 8
## 312 5 13
## 315 2 2
## 316 0 7
## 322 8 8
## 323 0 3
## 327 1 10
## 336 3 8
## 337 0 2
## 339 0 0
## 342 2 2
## 355 13 5
## 356 3 12
## 359 7 8
## 366 7 7
## 370 0 7
## 374 7 7
## 376 13 9
## 386 5 11
## 387 0 8
## 388 1 2
## 396 2 2
## 398 2 2
## 402 0 2
## 406 0 0
## 410 1 0
## 426 0 0
## 447 2 9
## 452 1 3
## 458 1 0
## 462 1 3
## 466 1 2
## 473 0 0
## 483 6 11
## 487 0 0
## 493 1 4
## 502 0 3
## 505 5 6
## 514 8 12
## 515 2 2
## 521 1 3
## 527 0 3
## 531 1 9
## 533 0 7
## 536 2 2
## 544 0 0
## 556 1 0
## 568 6 7
## 570 1 5
## 576 0 7
## 579 3 8
## 584 7 5
## 603 1 7
## 606 1 3
## 609 0 7
## 611 0 0
## 612 0 7
## 614 0 0
## 616 4 13
## 617 0 2
## 625 0 0
## 626 0 3
## 630 7 7
## 632 2 1
## 644 3 0
## 651 1 2
## 655 1 2
## 668 1 3
## 669 1 1
## 684 1 4
## 686 0 8
## 689 1 0
## 695 1 3
## 703 4 12
## 704 1 4
## 706 1 10
## 709 0 4
## 711 0 8
## 717 1 4
## 718 1 7
## 721 7 8
## 722 0 2
## 724 7 8
## 725 0 0
## 735 0 0
## 743 3 7
## 746 0 0
## 748 4 9
## 749 1 2
## 750 0 2
## 751 0 2
## 756 1 4
## 759 0 0
## 764 0 13
## 780 2 2
## 783 0 3
## 787 2 2
## 791 1 2
## 798 0 0
## 835 15 0
## 837 1 3
## 839 14 10
## 849 1 0
## 851 2 2
## 855 0 2
## 861 7 7
## 867 2 2Model1_fit <- lm(MonthlyIncome ~ ., data = Attritiondata)
summary(Model1_fit)##
## Call:
## lm(formula = MonthlyIncome ~ ., data = Attritiondata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3708.8 -674.1 14.7 614.1 4100.0
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.013e+01 7.772e+02 0.077 0.938349
## ID -2.343e-01 1.476e-01 -1.588 0.112713
## Age -1.431e+00 5.649e+00 -0.253 0.800110
## AttritionYes 8.904e+01 1.154e+02 0.771 0.440729
## BusinessTravelTravel_Frequently 1.895e+02 1.420e+02 1.334 0.182441
## BusinessTravelTravel_Rarely 3.720e+02 1.200e+02 3.099 0.002005 **
## DailyRate 1.452e-01 9.129e-02 1.591 0.112062
## DepartmentResearch & Development 1.234e+02 4.768e+02 0.259 0.795866
## DepartmentSales -4.594e+02 4.877e+02 -0.942 0.346580
## DistanceFromHome -6.237e+00 4.578e+00 -1.362 0.173417
## Education -3.743e+01 3.716e+01 -1.007 0.314105
## EducationFieldLife Sciences 1.352e+02 3.692e+02 0.366 0.714248
## EducationFieldMarketing 1.377e+02 3.914e+02 0.352 0.725050
## EducationFieldMedical 3.326e+01 3.699e+02 0.090 0.928376
## EducationFieldOther 9.152e+01 3.946e+02 0.232 0.816664
## EducationFieldTechnical Degree 9.680e+01 3.843e+02 0.252 0.801179
## EmployeeCount NA NA NA NA
## EmployeeNumber 8.681e-02 6.103e-02 1.422 0.155269
## EnvironmentSatisfaction -6.267e+00 3.364e+01 -0.186 0.852252
## GenderMale 1.100e+02 7.442e+01 1.478 0.139715
## HourlyRate -3.591e-01 1.824e+00 -0.197 0.844003
## JobInvolvement 1.677e+01 5.321e+01 0.315 0.752698
## JobLevel 2.783e+03 8.340e+01 33.375 < 2e-16 ***
## JobRoleHuman Resources -2.053e+02 5.157e+02 -0.398 0.690663
## JobRoleLaboratory Technician -5.891e+02 1.714e+02 -3.437 0.000618 ***
## JobRoleManager 4.280e+03 2.830e+02 15.122 < 2e-16 ***
## JobRoleManufacturing Director 1.809e+02 1.696e+02 1.067 0.286497
## JobRoleResearch Director 4.077e+03 2.193e+02 18.592 < 2e-16 ***
## JobRoleResearch Scientist -3.494e+02 1.705e+02 -2.049 0.040790 *
## JobRoleSales Executive 5.263e+02 3.576e+02 1.472 0.141449
## JobRoleSales Representative 8.531e+01 3.918e+02 0.218 0.827703
## JobSatisfaction 3.278e+01 3.344e+01 0.980 0.327278
## MaritalStatusMarried 6.708e+01 1.002e+02 0.669 0.503497
## MaritalStatusSingle 1.128e+01 1.361e+02 0.083 0.933978
## MonthlyRate -9.505e-03 5.143e-03 -1.848 0.064946 .
## NumCompaniesWorked 5.421e+00 1.691e+01 0.321 0.748622
## OverTimeYes -1.394e+01 8.434e+01 -0.165 0.868787
## PercentSalaryHike 2.586e+01 1.581e+01 1.635 0.102351
## PerformanceRating -3.235e+02 1.614e+02 -2.004 0.045368 *
## RelationshipSatisfaction 1.640e+01 3.339e+01 0.491 0.623375
## StandardHours NA NA NA NA
## StockOptionLevel -2.758e+00 5.740e+01 -0.048 0.961695
## TotalWorkingYears 5.080e+01 1.098e+01 4.627 4.3e-06 ***
## TrainingTimesLastYear 2.436e+01 2.912e+01 0.837 0.403111
## WorkLifeBalance -3.472e+01 5.161e+01 -0.673 0.501284
## YearsAtCompany -2.750e+00 1.370e+01 -0.201 0.840925
## YearsInCurrentRole 3.398e+00 1.711e+01 0.199 0.842584
## YearsSinceLastPromotion 3.084e+01 1.532e+01 2.013 0.044405 *
## YearsWithCurrManager -2.691e+01 1.669e+01 -1.613 0.107210
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1055 on 823 degrees of freedom
## Multiple R-squared: 0.9501, Adjusted R-squared: 0.9473
## F-statistic: 340.7 on 46 and 823 DF, p-value: < 2.2e-16#Model1_Preds = predict(Model1_fit, newdata = NoSalary)
#as.data.frame(Model1_Preds)
#write.csv(Model1_Preds,"Model1PredictionsNoSalaryRenuKarthikeyan")
#Cross Validation and Mean Square Predictor Error Calculation
numMSPEs = 1000
MSPEHolderModel1 = numeric(numMSPEs)
MSPEHolderModel2 = numeric(numMSPEs)
RMSEHolderModel1 = numeric(numMSPEs)
RMSEHolderModel2 = numeric(numMSPEs)
for (i in 1:numMSPEs)
{
TrainObs = sample(seq(1,dim(Attritiondata)[1]),round(.8*dim(Attritiondata)[1]),replace = FALSE)
SalaryTrain = Attritiondata[TrainObs,]
SalaryTrain
SalaryTest = Attritiondata[-TrainObs,]
SalaryTest
Model1_fit <- lm(MonthlyIncome ~ ., data = SalaryTrain)
Model1_Preds = predict(Model1_fit, newdata = SalaryTest)
#MSPE Model 1
MSPE = mean((SalaryTest$MonthlyIncome - Model1_Preds)^2)
MSPE
MSPEHolderModel1[i] = MSPE
RMSEHolderModel1[i] = sqrt(MSPE)
#Model 2
Model2_fit = lm(MonthlyIncome ~ NumCompaniesWorked + Age + Gender + MaritalStatus + JobInvolvement + JobRole + DistanceFromHome + JobLevel + Education, data = SalaryTrain)
Model2_Preds = predict(Model2_fit,newdata = SalaryTest)
MSPE = mean((SalaryTest$MonthlyIncome - Model2_Preds)^2)
MSPE
MSPEHolderModel2[i] = MSPE
RMSEHolderModel2[i] = sqrt(MSPE)
}
mean(MSPEHolderModel1)## [1] 1196649 mean(MSPEHolderModel2)## [1] 1194877 mean(RMSEHolderModel1)## [1] 1091.936 mean(RMSEHolderModel2)## [1] 1091.156- Data was split 80/20 – for training and testing
- Ran both model 1 and 2 to predict Monthly Income on the testing set
- Cross Validation Process with 1000 iterations where MSPE and RMSE are calculated for both models
- Summary Statistics of mean MSPE and mean RMSE for both models
Conclusion: Model 2 is the better fit as it has a lower mean RMSE and lower mean MSPE
KNN and Naive Bayes
When I initially attempted to use all predictors for KNN, each time, the model would unfortunately run into errors and say “Warning: NAs introduced by coercionError in knn(train_predictors, test_predictors, response_train, prob = TRUE,: NA/NaN/Inf in foreign function call (arg 6)”, although there are no missing values in the Attritiondata data set. I later realized that it was due to NAs being assigned to the categorical variables during the KNN chunk. I created dummy columns for these categorical variables to get the KNN to function without running into errors. Below is the code used to create the dummy columns from the FastDummies package in R.
Preparation of the Data - Including Dummy Columns for Categorical Variables
Attritiondata$BusinessTravel<- as.factor(Attritiondata$BusinessTravel)
Attritiondata$Department<- as.factor(Attritiondata$Department)
Attritiondata$EducationField<- as.factor(Attritiondata$EducationField)
Attritiondata$Gender<- as.factor(Attritiondata$Gender)
Attritiondata$JobRole<- as.factor(Attritiondata$JobRole)
Attritiondata$MaritalStatus<- as.factor(Attritiondata$MaritalStatus)
Attritiondata$OverTime<- as.factor(Attritiondata$OverTime)
Attritiondata<-dummy_cols(Attritiondata,select_columns=c("BusinessTravel","MaritalStatus","JobRole","Department","EducationField","OverTime","Gender"))
Attritiondata <- Attritiondata %>% select(-c("BusinessTravel","MaritalStatus","JobRole","Department","EducationField","OverTime","Gender"))
#Doing Same for Attrition Test Data Set (AttritionTest)
AttritionTest$BusinessTravel<- as.factor(AttritionTest$BusinessTravel)
AttritionTest$Department<- as.factor(AttritionTest$Department)
AttritionTest$EducationField<- as.factor(AttritionTest$EducationField)
AttritionTest$Gender<- as.factor(AttritionTest$Gender)
AttritionTest$JobRole<- as.factor(AttritionTest$JobRole)
AttritionTest$MaritalStatus<- as.factor(AttritionTest$MaritalStatus)
AttritionTest$OverTime<- as.factor(AttritionTest$OverTime)
AttritionTest<-dummy_cols(AttritionTest,select_columns=c("BusinessTravel","MaritalStatus","JobRole","Department","EducationField","OverTime","Gender"))
AttritionTest <- AttritionTest %>% select(-c("BusinessTravel","MaritalStatus","JobRole","Department","EducationField","OverTime","Gender"))
diff_columns_df1 <- setdiff(names(Attritiondata), names(AttritionTest))
cat("Columns in df2 but not in df1:", paste(diff_columns_df1, collapse = ", "), "\n")## Columns in df2 but not in df1: AttritionKNN Model and Confusion Matrix - Training with All predictors
set.seed(1234)
iterations <- 100
numks <- 10
splitPerc <- 0.8
masterAcc <- matrix(nrow = iterations, ncol = numks)
for (j in 1:iterations) {
trainIndices <- sample(1:dim(Attritiondata)[1], round(splitPerc * dim(Attritiondata)[1]))
train <- as.data.frame(Attritiondata[trainIndices, ])
test <- as.data.frame(Attritiondata[-trainIndices, ])
response_variable <- "Attrition"
response_train <- factor(train[[response_variable]])
response_test <- factor(test[[response_variable]])
# Select columns for predictors
selected_columns <- c(1, 2, 4:36) # Adjust this range as needed
#selected_columns <- c("ID","Age","BusinessTravel","DailyRate","Department", "DistanceFromHome", "Education", "EducationField", "EmployeeCount", "EmployeeNumber","EnvironmentSatisfaction", "Gender", "HourlyRate", "JobInvolvement", "JobLevel", "JobRole", "JobSatisfaction", "MaritalStatus", "MonthlyIncome", "MonthlyRate", "NumCompaniesWorked", "OverTime", "PercentSalaryHike", "PerformanceRating", "RelationshipSatisfaction", "StandardHours", "StockOptionLevel", "TotalWorkingYears", "TrainingTimesLastYear", "WorkLifeBalance", "YearsAtCompany", "YearsInCurrentRole", "YearsSinceLastPromotion", "YearsWithCurrManager")
# Extract the selected columns
train_predictors <- train[, selected_columns, drop = FALSE]
test_predictors <- test[, selected_columns, drop = FALSE]
train_predictors <- apply(train_predictors, 2, as.numeric)
test_predictors <- apply(test_predictors, 2, as.numeric)
train_predictors <- scale(train_predictors)
test_predictors <- scale(test_predictors)
# Convert to numeric matrices
train_predictors <- as.matrix(train_predictors)
test_predictors <- as.matrix(test_predictors)
# Remove infinite values
train_predictors[!is.finite(train_predictors)] <- 0
test_predictors[!is.finite(test_predictors)] <- 0
if (sum(response_train == "Yes") > 0 && sum(response_test == "Yes") > 0) {
for (i in 1:numks) {
classifications <- knn(train_predictors, test_predictors, response_train, prob = TRUE, k = i)
table(classifications, response_test)
CM_AllK<- confusionMatrix(table(classifications, response_test), positive = "Yes")
masterAcc[j, i] <- CM_AllK$overall[1]
}
}
}
CM_AllK## Confusion Matrix and Statistics
##
## response_test
## classifications No Yes
## No 143 26
## Yes 2 3
##
## Accuracy : 0.8391
## 95% CI : (0.7759, 0.8903)
## No Information Rate : 0.8333
## P-Value [Acc > NIR] : 0.4685
##
## Kappa : 0.134
##
## Mcnemar's Test P-Value : 1.383e-05
##
## Sensitivity : 0.10345
## Specificity : 0.98621
## Pos Pred Value : 0.60000
## Neg Pred Value : 0.84615
## Prevalence : 0.16667
## Detection Rate : 0.01724
## Detection Prevalence : 0.02874
## Balanced Accuracy : 0.54483
##
## 'Positive' Class : Yes
## MeanAcc = colMeans(masterAcc); MeanAcc## [1] 0.7946552 0.7893103 0.8301724 0.8294828 0.8386782 0.8394828 0.8433333
## [8] 0.8406897 0.8425287 0.8426437plot(seq(1, numks, 1), MeanAcc, type = "l", ylab = "Mean Accuracy (Positive Class: Yes)")
which.max(MeanAcc)## [1] 7max(MeanAcc)## [1] 0.8433333From the plot, we see that the best k is k = 7. The overall accuracy is 83.91%, but sensitivity (True Positive Rate) is low (10.35%).The model is better at correctly predicting the majority class (“No”) but struggles with the minority class (“Yes”).
Looking specifically at the Confusion Matrix statistics, this is the output and the interpretation of each of these statistics: - Sensitivity (True Positive Rate): 0.10345 The proportion of actual positives correctly predicted for those who attrited. - Specificity (True Negative Rate): 0.98621 The proportion of actual negatives correctly predicted (for those who did not leave the company) - Positive Predictive Value (Precision): 0.60000 The proportion of predicted positives that are true positives (attrited correctly identified as attrited) - Negative Predictive Value: 0.84615 The proportion of predicted negatives that are true negatives. (not attrited correctly identified as not attrited) - Prevalence: 0.16667 The proportion of actual positives in the dataset. (proportion of attrited in the overall dataset)
TRYING THRESHOLD CHANGE for KNN - All predictors; k = 7
set.seed(1234)
iterations<- 100
accuracy_table <- numeric(iterations)
trainIndices <- sample(1:dim(Attritiondata)[1], round(splitPerc * dim(Attritiondata)[1]))
accuracy_table <- numeric(iterations)
train <- as.data.frame(Attritiondata[trainIndices, ])
test <- as.data.frame(Attritiondata[-trainIndices, ])
train_features<- train[, -which(names(train) == "Attrition")]
test_features<- test[, -which(names(test) == "Attrition")]
train_target<- train$Attrition
# train_scaled<- scale(train_features) Using this in the knn returns "No missing values allowed"
#test_scaled<- scale(test_features)
for (i in 1:iterations) {
classifications <- knn(train_features, test_features, train_target, prob = TRUE, k = 7)
table(classifications, response_test)
CM_AllK7 <- confusionMatrix(table(as.factor(test$Attrition),classifications), positive ="Yes")
accuracy_table[i] <- CM_AllK7$overall[1]
}
#print(accuracy_table)
avg_accuracy<-mean(accuracy_table[1])
avg_accuracy## [1] 0.7988506specificity_table<- numeric(iterations)
sensitivity_table<- numeric(iterations)
for (i in 1:iterations) {
classifications <- knn(train_features, test_features, train_target, prob = TRUE, k = 7)
table(classifications, response_test)
CM_AllK7 <- confusionMatrix(table(as.factor(test$Attrition),classifications), positive ="Yes")
specificity_table[i] <- CM_AllK7$byClass['Specificity']
sensitivity_table[i] <- CM_AllK7$byClass['Sensitivity']
}
CM_AllK7## Confusion Matrix and Statistics
##
## classifications
## No Yes
## No 137 3
## Yes 32 2
##
## Accuracy : 0.7989
## 95% CI : (0.7315, 0.8557)
## No Information Rate : 0.9713
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0552
##
## Mcnemar's Test P-Value : 2.214e-06
##
## Sensitivity : 0.40000
## Specificity : 0.81065
## Pos Pred Value : 0.05882
## Neg Pred Value : 0.97857
## Prevalence : 0.02874
## Detection Rate : 0.01149
## Detection Prevalence : 0.19540
## Balanced Accuracy : 0.60533
##
## 'Positive' Class : Yes
## avg_specificity<-mean(specificity_table[1])
avg_specificity## [1] 0.8106509avg_sensitivity<-mean(sensitivity_table[1])
avg_sensitivity## [1] 0.4###### New Threshold using classifications which used k = 7 from above
#classifications
#attributes(classifications) # Look at possible attributes
#attributes(classifications)$prob # Probability of what was classified for that observation
probs = ifelse(classifications == "Yes",attributes(classifications)$prob, 1- attributes(classifications)$prob)
summary(Attritiondata$Attrition)## No Yes
## 730 140140/(730+140) #16.09%## [1] 0.1609195NewClass = ifelse(probs > .1609, "Yes", "No")
NewClass <- factor(NewClass, levels = levels(response_test))
table(NewClass,response_test)## response_test
## NewClass No Yes
## No 115 18
## Yes 30 11CM_NewThreshold <- confusionMatrix(table(NewClass, response_test), positive = "Yes", mode = "everything")
CM_NewThreshold## Confusion Matrix and Statistics
##
## response_test
## NewClass No Yes
## No 115 18
## Yes 30 11
##
## Accuracy : 0.7241
## 95% CI : (0.6514, 0.7891)
## No Information Rate : 0.8333
## P-Value [Acc > NIR] : 0.9999
##
## Kappa : 0.1479
##
## Mcnemar's Test P-Value : 0.1124
##
## Sensitivity : 0.37931
## Specificity : 0.79310
## Pos Pred Value : 0.26829
## Neg Pred Value : 0.86466
## Precision : 0.26829
## Recall : 0.37931
## F1 : 0.31429
## Prevalence : 0.16667
## Detection Rate : 0.06322
## Detection Prevalence : 0.23563
## Balanced Accuracy : 0.58621
##
## 'Positive' Class : Yes
## Similar to before, the loop ran for 100 iterations, but k was set to 7 when it came to knn. There are 2 confusion matrices. We have one, without the threshold changes, and one with the threshold change. The accuracy went down with the threshold change, while sensitivity reduced by a lot, and specificity reduced by ~10%. The positive predictive value increased by 9%, and the negative pred value reduced by 14%.
The new threshold has a lower accuracy compared to the original kNN classification.Sensitivity is significantly lower for the new threshold, indicating that fewer true positives are captured.Specificity is slightly lower for the new threshold, indicating a decrease in correctly identified true negatives. Precision is lower for the new threshold, reflecting a decrease in the accuracy of positive predictions. The original KNN classification has a higher accuracy and sensitivity compared to the new threshold.
#Applying to the Test Model to predict attrition using KNN with all predictors (This is best model)
train_features<- Attritiondata[, -which(names(Attritiondata) == "Attrition")]
test_features<- AttritionTest[]#[, -which(names(AttritionTest)=="Attrition")]
#head(test_features,5)
train_target <- Attritiondata$Attrition
AttritionClassifications <- knn(train_features, test_features, train_target, prob = TRUE, k = 7)
AttritionTest$Attrition <- AttritionClassifications
#head(AttritionTest,5)
AttritionPredictionsKNN<-AttritionTest%>%select(c("ID","Attrition"))
#head(AttritionPredictionsKNN,5)
write.csv(AttritionPredictionsKNN,"CaseStudy2AttritionPredictionsKNN_RenuKarthikeyan.csv", row.names = T)KNN Using Select Predictors
After doing my Exploratory Data Analysis, I believe the important predictors for Attrition are: Gender, Department,Job Satisfaction,Distance From Home,Monthly Income, Job Level, Age, Over Time, Percent Salary Hike, Performance Rating, and Education.
set.seed(1234)
iterations = 100
numks = 10
splitPerc = .8
masterAcc = matrix(nrow = iterations, ncol = numks)
for (j in 1:iterations) {
trainIndices <- sample(1:dim(Attritiondata)[1], round(splitPerc * dim(Attritiondata)[1]))
train <- as.data.frame(Attritiondata[trainIndices, ])
test <- as.data.frame(Attritiondata[-trainIndices, ])
response_train <- factor(train$Attrition)
response_test <- factor(test$Attrition)
#UPDATE HERE!!!!
#train_predictors <- train[, c(2,6,7,8,13,16,18,20,24,25,26)]
#test_predictors <- test[, c(2,6,7,8,13,16,18,20,24,25,26) ]
selected_cols <- c(2, 6, 7, 8, 13, 16, 18, 20, 24, 25, 26)
train_predictors <- train[, selected_cols] #subset function
test_predictors <- test[, selected_cols]
train_predictors <- apply(train_predictors, 2, as.numeric)
test_predictors <- apply(test_predictors, 2, as.numeric)
train_predictors <- scale(train_predictors)
test_predictors <- scale(test_predictors)
train_predictors <- as.matrix(train_predictors)
test_predictors <- as.matrix(test_predictors)
# Remove infinite values
train_predictors[!is.finite(train_predictors)] <- 0
test_predictors[!is.finite(test_predictors)] <- 0
for (i in 1:numks) {
classifications <- knn(train_predictors, test_predictors, response_train, prob =TRUE, k = i)
table(classifications, response_test)
CM_Select<- confusionMatrix(table(classifications, response_test), positive = "Yes")
masterAcc[j, i] <- CM_Select$overall[1]
}
}
CM_Select## Confusion Matrix and Statistics
##
## response_test
## classifications No Yes
## No 144 24
## Yes 4 2
##
## Accuracy : 0.8391
## 95% CI : (0.7759, 0.8903)
## No Information Rate : 0.8506
## P-Value [Acc > NIR] : 0.7085939
##
## Kappa : 0.0731
##
## Mcnemar's Test P-Value : 0.0003298
##
## Sensitivity : 0.07692
## Specificity : 0.97297
## Pos Pred Value : 0.33333
## Neg Pred Value : 0.85714
## Prevalence : 0.14943
## Detection Rate : 0.01149
## Detection Prevalence : 0.03448
## Balanced Accuracy : 0.52495
##
## 'Positive' Class : Yes
## MeanAcc = colMeans(masterAcc); MeanAcc## [1] 0.7545977 0.7523563 0.8077586 0.8095977 0.8272414 0.8263793 0.8315517
## [8] 0.8305172 0.8338506 0.8359195plot(seq(1, numks, 1), MeanAcc, type = "l", ylab = "Mean Accuracy (Positive Class: Yes)")
which.max(MeanAcc)## [1] 10max(MeanAcc)## [1] 0.8359195We see that the best k is k = 10. The confusion matrix is taking an average of all the k’s tried. Here, the accuracy is 83.91%, similar to the initial average confusion matrix seen for all predictors using KNN. The sensitivity is quite low at 7.69%, suggesting that the model is struggling to correctly identify positive (true Attrition) instances. The model shows high specificity (97.30%), indicating a decent ability to correctly identify negative instances (not attrition). The positive predictive value (precision) is at 33.33%, indicating that among instances predicted as positive, about one-third are true positives.
TRYING k = 10 for KNN - Select predictors
iterations<- 100
accuracy_table <- numeric(iterations)
trainIndices <- sample(1:dim(Attritiondata)[1], round(splitPerc * dim(Attritiondata)[1]))
train <- as.data.frame(Attritiondata[trainIndices, ])
test <- as.data.frame(Attritiondata[-trainIndices, ])
train_features<- train[, selected_cols]
test_features<- test[, selected_cols]
train_target<- train$Attrition
train_scaled<- scale(train_features)
test_scaled<- scale(test_features)
for (i in 1:iterations) {
classifications <- knn(train_features, test_features, train_target, prob = TRUE, k = 10)
table(classifications, response_test)
CM_SelectK <- confusionMatrix(table(as.factor(test$Attrition),classifications), positive ="Yes")
accuracy_table[i] <- CM_SelectK$overall[1]
}
#print(accuracy_table)
avg_accuracy<-mean(accuracy_table[1]); avg_accuracy## [1] 0.8275862specificity_table<- numeric(iterations)
sensitivity_table<- numeric(iterations)
for (i in 1:iterations) {
classifications <- knn(train_features, test_features, train_target, prob = TRUE, k = 10)
table(classifications, response_test)
CM_SelectK<- confusionMatrix(table(as.factor(test$Attrition),classifications), positive ="Yes")
specificity_table[i] <- CM_SelectK$byClass['Specificity']
sensitivity_table[i] <- CM_SelectK$byClass['Sensitivity']
}
CM_SelectK## Confusion Matrix and Statistics
##
## classifications
## No Yes
## No 145 0
## Yes 29 0
##
## Accuracy : 0.8333
## 95% CI : (0.7695, 0.8854)
## No Information Rate : 1
## P-Value [Acc > NIR] : 1
##
## Kappa : 0
##
## Mcnemar's Test P-Value : 1.999e-07
##
## Sensitivity : NA
## Specificity : 0.8333
## Pos Pred Value : NA
## Neg Pred Value : NA
## Prevalence : 0.0000
## Detection Rate : 0.0000
## Detection Prevalence : 0.1667
## Balanced Accuracy : NA
##
## 'Positive' Class : Yes
## avg_specificity<-mean(specificity_table[1])
avg_specificity## [1] 0.8323699avg_sensitivity<-mean(sensitivity_table[1])
avg_sensitivity## [1] 0We see the average specificity is 83.43% and sensitivity is 0%. This model’s performance is notably poor, with zero sensitivity, meaning it failed to correctly identify any positive instances. The specificity and negative predictive value are relatively high, but the lack of sensitivity indicates a serious limitation in identifying instances of the positive class. This suggests that the model might need further refinement or a different approach to address the imbalance and improve its ability to correctly classify positive instances.
Naive Bayes and Confusion Matrix - Training with All predictors
set.seed(1234)
iterations = 100
masterAcc = matrix(nrow = iterations)
splitPerc = .8 #Training / Test split Percentage
for(j in 1:iterations)
{
trainIndices <- sample(1:dim(Attritiondata)[1], round(splitPerc * dim(Attritiondata)[1]))
train <- as.data.frame(Attritiondata[trainIndices, ])
test <- as.data.frame(Attritiondata[-trainIndices, ])
train$Attrition <- factor(train$Attrition, levels = c("Yes", "No"))
test$Attrition <- factor(test$Attrition, levels = c("Yes", "No"))
model <- naiveBayes(train[, -3], as.factor(train$Attrition), laplace = 1)
predictions <- predict(model, test[, -3])
confMatrix <- table(predictions, as.factor(test$Attrition))
CM_NB_All <- confusionMatrix(confMatrix)
masterAcc[j] <- CM_NB_All$overall[1]
}
CM_NB_All## Confusion Matrix and Statistics
##
##
## predictions Yes No
## Yes 23 92
## No 2 57
##
## Accuracy : 0.4598
## 95% CI : (0.3841, 0.5368)
## No Information Rate : 0.8563
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.1211
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.9200
## Specificity : 0.3826
## Pos Pred Value : 0.2000
## Neg Pred Value : 0.9661
## Prevalence : 0.1437
## Detection Rate : 0.1322
## Detection Prevalence : 0.6609
## Balanced Accuracy : 0.6513
##
## 'Positive' Class : Yes
## MeanAcc = colMeans(masterAcc); MeanAcc## [1] 0.6482184Given all the predictors, the sensitivity rate of the model is the percentage of actual attrition cases correctly identified. It measures the model’s ability to capture employees who are truly at risk of attrition among all employees who actually attrite. This model shows imbalanced performance with high sensitivity (high attrition) but low specificity. It performs well in identifying actual positive instances (attrition) but struggles to correctly identify negative instances(no attrition). The positive predictive value is relatively low, indicating that when it predicts a positive instance, it has a 20% chance of being correct.
Naive Bayes and Confusion Matrix - Training; Using Select predictors to determine Attrition
set.seed(1234)
iterations = 100
masterAcc = matrix(nrow = iterations)
splitPerc = .8 #Training / Test split Percentage
for(j in 1:iterations)
{
trainIndices <- sample(1:dim(Attritiondata)[1], round(splitPerc * dim(Attritiondata)[1]))
train <- as.data.frame(Attritiondata[trainIndices, ])
test <- as.data.frame(Attritiondata[-trainIndices, ])
train$Attrition <- factor(train$Attrition, levels = c("Yes", "No"))
test$Attrition <- factor(test$Attrition, levels = c("Yes", "No"))
model2 <- naiveBayes(train[, c(2, 6, 7, 8, 13, 16, 18, 20, 24, 25, 26)], as.factor(train$Attrition), laplace = 1)
predictions <- predict(model2, test[, c(2, 6, 7, 8, 13, 16, 18, 20, 24, 25, 26)])
confMatrix <- table(predictions, as.factor(test$Attrition))
CM_NB_Select <- confusionMatrix(confMatrix)
masterAcc[j] <- CM_NB_Select$overall[1]
}
CM_NB_Select## Confusion Matrix and Statistics
##
##
## predictions Yes No
## Yes 3 4
## No 22 145
##
## Accuracy : 0.8506
## 95% CI : (0.7888, 0.9)
## No Information Rate : 0.8563
## P-Value [Acc > NIR] : 0.6357048
##
## Kappa : 0.133
##
## Mcnemar's Test P-Value : 0.0008561
##
## Sensitivity : 0.12000
## Specificity : 0.97315
## Pos Pred Value : 0.42857
## Neg Pred Value : 0.86826
## Prevalence : 0.14368
## Detection Rate : 0.01724
## Detection Prevalence : 0.04023
## Balanced Accuracy : 0.54658
##
## 'Positive' Class : Yes
## MeanAcc = colMeans(masterAcc)
MeanAcc## [1] 0.8312069#use predict function on the "validation" sets. Use the same model. Test will be validation set.
Predictions<- predict(model2,AttritionTest[,c(2, 5, 6, 7, 12, 15, 17, 19, 23, 24, 25)])
AttritionTest$Attrition<- Predictions
#View(AttritionTest$Attrition)
AttritionPredictionsNB<- AttritionTest %>% select(c("ID","Attrition"))
write.csv(AttritionPredictionsNB,"CaseStudy2AttritionPredictionsNB_RenuKarthikeyan.csv", row.names = T)Sensitivity is 12%; 12% of actual attrition cases are correctly identified by the model. This suggests that the model may not be very effective at capturing employees who are truly at risk of attrition. This model has a higher accuracy of 85.06%, and has imbalanced performance with low sensitivity and high specificity. However, accuracy can be misleading, especially in imbalanced datasets where one class (e.g., “No attrition”) dominates. In this case, accuracy is not the best metric to evaluate the model’s performance. Positive predictive value (PPV) is at 42.86%. This indicates that when the model predicts attrition, there’s a 42.86% chance that the prediction is correct. It reflects the precision of the model in identifying true positive cases among all instances predicted as positive. The low prevalence (the proportion of actual positive cases in the dataset) of attrition, is 14.37%. This low prevalence contributes to the imbalanced nature of the performance metrics.
Best Model Determination from the Models tried for Attrition (KNN and Naive Bayes)
It looks like the KNN model at k = 7 with all predictors without threshold adjustment has better accuracy within the KNN models. The Naive Bayes model with select predictors has the highest accuracy, and is better than the other Naive Bayes model which included all predictors.
Naive Bayes has a higher accuracy (85.06%) compared to KNN (79.89%). However, accuracy alone may not be the most informative metric, especially in imbalanced datasets.KNN has a higher sensitivity (40.00%) compared to Naive Bayes (12.00%). Sensitivity is crucial when identifying cases of attrition, as it represents the proportion of true positive cases among all actual positive cases.Naive Bayes has higher specificity (97.32%) compared to KNN (81.07%). Specificity is important when minimizing false positives, but it’s essential to balance it with sensitivity.Naïve Bayes has a higher positive predictive value (precision) at 42.86%, while KNN has a lower precision at 5.88%. Precision indicates the accuracy of positive predictions.
Of the 2 best models (best in KNN and best in Naive Bayes), I think the Naive Bayes is the better model to predict Attrition, given the high positive prediction value(precision), accuracy, and narrower confidence interval.
Thank You
This concludes this presentation and analysis. Thank you for your time and I look forward to empowering Frito Lay with data-driven wisdom. I created a shiny app to visualize and notice insights regarding the attrition data. Please feel free to look into the link provided. If you have any questions, please feel free to reach out to me, my email is in the attached PowerPoint presentation. Thank you!